Method and device for video processing and medium

Abstract

Embodiments of the present disclosure provide a scheme for video processing. A method for video processing is presented. The method includes determining, for a conversion between a video unit of a video and a bitstream of the video unit, a gradient from one or more directions associated with the video unit, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and performing the conversion based on the prediction of the video unit.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to video processing technologies, and more particularly, to a convolutional cross-component model. Background Art

[0002] Nowadays, digital video capabilities are being applied to all aspects of people's lives. For video encoding / decoding, various types of video compression technologies have been proposed, such as MPEG-2, MPEG-4, ITU-T H.263, ITU-T H.264 / MPEG-4 Part 10 Advanced Video Coding (AVC), ITU-T H.265 High Efficiency Video Coding (HEVC) standard, and Versatile Video Coding (VVC) standard. However, there is generally a desire to further improve the encoding / decoding efficiency of video encoding / decoding technologies. Summary of the Invention

[0003] Embodiments of the present disclosure provide a solution for video processing.

[0004] In a first aspect, a method for video processing is proposed. The method includes: determining gradients from one or more directions associated with a video unit for the conversion between the video unit of a video and the bitstream of the video unit, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from one or more directions; and performing the conversion based on the prediction of the video unit. In this way, the encoding / decoding performance and encoding / decoding efficiency can be improved.

[0005] In a second aspect, another method for video processing is proposed. The method includes: applying a linear model (LM) mode to a video unit based on the non-downsampled luminance value for the conversion between the video unit of a video and the bitstream of the video unit; determining a prediction of the video unit based on the LM mode; and performing the conversion based on the prediction of the video unit. In this way, the encoding / decoding performance and encoding / decoding efficiency can be improved.

[0006] In a third aspect, an apparatus for video processing is proposed. The apparatus includes a processor and a non-transitory memory having instructions. When the instructions are executed by the processor, the processor is caused to execute the method according to the first aspect or the second aspect of the present disclosure.

[0007] In a fourth aspect, a non-transitory computer-readable storage medium is proposed. The non-transitory computer-readable storage medium stores instructions that cause a processor to execute the method according to the first aspect or the second aspect of the present disclosure.

[0008] In a fifth aspect, another non-transitory computer-readable recording medium is proposed. The non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing. The method includes: determining gradients from one or more directions associated with a video unit of the video, wherein a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and generating a bitstream based on the prediction of the video unit.

[0009] In a sixth aspect, a method for storing a bitstream of a video is proposed. The method includes: determining gradients from one or more directions associated with a video unit of the video, wherein a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; generating a bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

[0010] In a seventh aspect, another non-transitory computer-readable recording medium is proposed. The non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing. The method includes applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; determining a prediction of the video unit based on the LM mode; and generating a bitstream based on the prediction of the video unit.

[0011] In an eighth aspect, a method for storing a bitstream of a video is proposed. The method includes: applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; determining a prediction of the video unit based on the LM mode; generating a bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

[0012] The present invention content is provided to introduce in a simplified form a selection of concepts further described below in the detailed implementation. The present invention content is not intended to identify the key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other objects, features, and advantages of the exemplary embodiments of the present disclosure will become more apparent from the following detailed description with reference to the accompanying drawings. In the exemplary embodiments of the present disclosure, the same reference numerals generally refer to the same components.

[0014] Figure 1 A block diagram showing an example video codec system according to some embodiments of the present disclosure is shown;

[0015] Figure 2Shows a block diagram showing a first example video encoder according to some embodiments of the present disclosure;

[0016] Figure 3 Shows a block diagram showing an example video decoder according to some embodiments of the present disclosure;

[0017] Figure 4 Shows the vertical and horizontal positions of 4:2:2 luma and chroma samples in a picture;

[0018] Figure 5 Shows an example of an encoder block diagram;

[0019] Figure 6 Shows 67 intra prediction modes;

[0020] Figure 7 Shows reference samples for wide-angle intra prediction;

[0021] Figure 8 Shows the problem of discontinuity in the case where the direction exceeds 45°;

[0022] Figure 9 Shows the positions of the samples used to derive α and β;

[0023] Figure 10 Shows an example of classifying neighboring samples into two groups;

[0024] Figure 11A Is a schematic diagram showing the definition of the samples used by PDPC applied to the diagonal upper right mode;

[0025] Figure 11B Is a schematic diagram showing the definition of the samples used by PDPC applied to the diagonal lower left mode;

[0026] Figure 11C Is a schematic diagram showing the definition of the samples used by PDPC applied to the adjacent diagonal upper right mode;

[0027] Figure 11D Is a schematic diagram showing the definition of the samples used by PDPC applied to the adjacent diagonal lower left mode;

[0028] Figure 12 Is a schematic diagram showing the gradient method for non-vertical / non-horizontal modes;

[0029] Figure 13 Is a schematic diagram showing the nScale value related to nTbH and the mode number; for all nScale < 0 cases, the gradient method is used;

[0030] Figure 14It is a schematic diagram showing the flowchart of the current PDPC and the proposed PDPC;

[0031] Figure 15 It is a schematic diagram showing the neighboring blocks (L, A, BL, AR, AL) for deriving the general MPM list;

[0032] Figure 16 It is a schematic diagram showing an example of the proposed intra-frame reference mapping;

[0033] Figure 17 It is a schematic diagram showing an example of four reference lines adjacent to the prediction block;

[0034] Figure 18A It is a schematic diagram showing an example of the sub-division for 4×8 and 8×4 CUs;

[0035] Figure 18B It is a schematic diagram showing an example of the sub-division for CUs other than 4×8, 8×4 and 4×4;

[0036] Figure 19 It is a schematic diagram showing the matrix weighted intra-frame prediction process;

[0037] Figure 20 It is a schematic diagram showing the target sample points, template sample points and reference sample points of the template used in DIMD;

[0038] Figure 21 It is a schematic diagram showing the proposed intra-frame block decoding process;

[0039] Figure 22 It is a schematic diagram showing the HoG calculation from a template with a width of 3 pixels;

[0040] Figure 23 It is a schematic diagram showing the prediction fusion weighted and averaged by two HoG modes and the plane mode;

[0041] Figure 24 It is a schematic diagram showing the spatial part of the convolutional filter;

[0042] Figure 25 It is a schematic diagram showing the reference region (with its filling) for deriving the filter coefficients;

[0043] Figure 26 It is a schematic diagram showing four Sobel-based gradient modes for GLM;

[0044] Figure 27 It is a schematic diagram showing the spatial sample points for GL-CCCM;

[0045] Figure 28 It is a schematic diagram showing the non-downsampled luminance sample points;

[0046] Figures 29A to 29F Shows the gradient calculated using a 3×3 shape;

[0047] Figures 30A to 30D Shows the gradient calculated using a 3×2 shape;

[0048] Figure 31 Shows a flowchart of a method for video processing according to an embodiment of the present disclosure;

[0049] Figure 32 Shows a flowchart of a method for video processing according to an embodiment of the present disclosure; and

[0050] Figure 33 Shows a block diagram of a computing device in which various embodiments of the present disclosure may be implemented.

[0051] Throughout all the figures, the same or similar reference numerals generally refer to the same or similar elements. Detailed Description

[0052] The principles of the present disclosure will now be described with reference to some embodiments. It should be understood that the description of these embodiments is for illustrative purposes only and to assist those skilled in the art in understanding and implementing the present disclosure, and does not imply any limitation on the scope of the present disclosure. The disclosure described herein may be implemented in various ways other than those described below.

[0053] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains.

[0054] As used in the present disclosure, the phrases "one embodiment", "an embodiment", "example embodiment", etc. indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment must include that particular feature, structure, or characteristic. Moreover, these phrases do not necessarily refer to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an example embodiment, it is contended that such feature, structure, or characteristic, whether or not explicitly described, is within the knowledge of those skilled in the art in relation to other embodiments.

[0055] It should be understood that although terms such as "first" and "second" may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0056] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the exemplary embodiments. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms "comprises", "comprising", "has", "having", "includes" and / or "including" when used herein specify the presence of the stated features, elements and / or components, etc., but do not preclude the presence or addition of one or more other features, elements, components and / or combinations thereof. Exemplary Environment

[0057] Figure 1 is a block diagram showing an exemplary video codec system 100 that can utilize the techniques of the present disclosure. As shown, the video codec system 100 may include a source device 110 and a destination device 120. The source device 110 may also be referred to as a video encoding device, and the destination device 120 may also be referred to as a video decoding device. In operation, the source device 110 may be configured to generate encoded video data, and the destination device 120 may be configured to decode the encoded video data generated by the source device 110. The source device 110 may include a video source 112, a video encoder 114, and an input / output (I / O) interface 116.

[0058] The video source 112 may include sources such as video capture devices. Examples of video capture devices include, but are not limited to, interfaces for receiving video data from video content providers, computer graphics systems for generating video data, and / or combinations thereof.

[0059] Video data can include one or more pictures. The video encoder 114 encodes the video data from the video source 112 to generate a bitstream. The bitstream can include a sequence of bits that forms an encoded representation of the video data. The bitstream can include encoded pictures and associated data. The encoded pictures are the encoded representations of the pictures. The associated data can include sequence parameter sets, picture parameter sets, and other syntax structures. The I / O interface 116 can include a modulator / demodulator and / or a transmitter. The encoded video data can be directly transmitted to the destination device 120 via the I / O interface 116 over the network 130A. The encoded video data can also be stored on the storage medium / server 130B for access by the destination device 120.

[0060] The destination device 120 can include an I / O interface 126, a video decoder 124, and a display device 122. The I / O interface 126 can include a receiver and / or a modulator. The I / O interface 126 can obtain the encoded video data from the source device 110 or the storage medium / server 130B. The video decoder 124 can decode the encoded video data. The display device 122 can display the decoded video data to the user. The display device 122 can be integrated with the destination device 120 or can be external to the destination device 120, which is configured to interface with an external display device.

[0061] The video encoder 114 and the video decoder 124 can operate according to video compression standards (such as the High Efficiency Video Coding (HEVC) standard, the Versatile Video Coding (VVC) standard, and other existing and / or future standards).

[0062] Figure 2 is a block diagram showing an example of a video encoder 200 according to some embodiments of the present disclosure. The video encoder 200 can be Figure 1 an example of the video encoder 114 in the system 100 shown.

[0063] The video encoder 200 can be configured to implement any or all of the techniques of the present disclosure. In Figure 2 an example, the video encoder 200 includes a plurality of functional components. The techniques described in the present disclosure can be shared among the various components of the video encoder 200. In some examples, a processor can be configured to execute any or all of the techniques described in the present disclosure.

[0064] In some embodiments, the video encoder 200 may include a splitting unit 201, a prediction unit 202, a residual generation unit 207, a transformation unit 208, a quantization unit 209, an inverse quantization unit 210, an inverse transformation unit 211, a reconstruction unit 212, a buffer 213, and an entropy encoding unit 214. The prediction unit 202 may include a mode selection unit 203, a motion estimation unit 204, a motion compensation unit 205, and an intra prediction unit 206.

[0065] In other examples, the video encoder 200 may include more, fewer, or different functional components. In one example, the prediction unit 202 may include an Intra Block Copy (IBC) unit. The IBC unit may perform prediction in an IBC mode in which at least one reference picture is the picture in which the current video block is located.

[0066] Furthermore, although some components (such as the motion estimation unit 204 and the motion compensation unit 205) may be integrated, for explanatory purposes, these components are shown separately in Figure 2 the examples.

[0067] The splitting unit 201 may split a picture into one or more video blocks. The video encoder 200 and the video decoder 300 may support various video block sizes.

[0068] The mode selection unit 203 may select, for example, one coding mode among multiple coding modes (intra coding or inter coding) based on an error result, and provide the resulting intra-coded block or inter-coded block to the residual generation unit 207 to generate residual block data, and provide it to the reconstruction unit 212 to reconstruct the coded block for use as a reference picture. In some examples, the mode selection unit 203 may select an Intra-Inter Combined Prediction (CIIP) mode in which the prediction is based on an inter prediction signal and an intra prediction signal. In the case of inter prediction, the mode selection unit 203 may also select a resolution for the motion vector for the block (e.g., sub-pixel accuracy or integer pixel accuracy).

[0069] To perform inter prediction on a current video block, the motion estimation unit 204 may generate motion information for the current video block by comparing one or more reference frames from the buffer 213 with the current video block. The motion compensation unit 205 may determine a predicted video block for the current video block based on the motion information and the decoded samples of a picture from the buffer 213 other than the picture associated with the current video block.

[0070] The motion estimation unit 204 and the motion compensation unit 205 can perform different operations on a current video block, e.g., depending on whether the current video block is in an I-slice, a P-slice, or a B-slice. As used herein, an "I-slice" can refer to a portion of a picture composed of macroblocks, all of which are based on macroblocks within the same picture. Additionally, as used herein, in some aspects, a "P-slice" and a "B-slice" can refer to portions of a picture composed of macroblocks independent of macroblocks in the same picture.

[0071] In some examples, the motion estimation unit 204 can perform uni-directional prediction on a current video block, and the motion estimation unit 204 can search the reference pictures in list 0 or list 1 to find a reference video block for the current video block. The motion estimation unit 204 can then generate a reference index and a motion vector, the reference index indicating the reference picture in list 0 or list 1 that contains the reference video block, and the motion vector indicating the spatial displacement between the current video block and the reference video block. The motion estimation unit 204 can output the reference index, the prediction direction indicator, and the motion vector as the motion information of the current video block. The motion compensation unit 205 can generate a predicted video block for the current video block based on the reference video block indicated by the motion information of the current video block.

[0072] Alternatively, in other examples, the motion estimation unit 204 can perform bi-directional prediction on a current video block. The motion estimation unit 204 can search the reference pictures in list 0 to find one reference video block for the current video block, and can also search the reference pictures in list 1 to find another reference video block for the current video block. The motion estimation unit 204 can then generate a plurality of reference indices and a plurality of motion vectors, the plurality of reference indices indicating the plurality of reference pictures in list 0 and list 1 that contain the plurality of reference video blocks, and the plurality of motion vectors indicating the plurality of spatial displacements between the plurality of reference video blocks and the current video block. The motion estimation unit 204 can output the plurality of reference indices and the plurality of motion vectors of the current video block as the motion information of the current video block. The motion compensation unit 205 can generate a predicted video block for the current video block based on the plurality of reference video blocks indicated by the motion information of the current video block.

[0073] In some examples, the motion estimation unit 204 can output a complete set of motion information for use in the decoder's decoding process. Alternatively, in some embodiments, the motion estimation unit 204 can signal the motion information of the current video block by referring to the motion information of another video block. For example, the motion estimation unit 204 can determine that the motion information of the current video block is sufficiently similar to the motion information of a neighboring video block.

[0074] In one example, the motion estimation unit 204 may indicate a value in the syntax structure associated with the current video block, and this value indicates to the video decoder 300 that the current video block has the same motion information as another video block.

[0075] In another example, the motion estimation unit 204 may identify another video block and a motion vector difference (MVD) in the syntax structure associated with the current video block. The motion vector difference indicates the difference between the motion vector of the current video block and the motion vector of the indicated video block. The video decoder 300 may use the motion vector of the indicated video block and the motion vector difference to determine the motion vector of the current video block.

[0076] As discussed above, the video encoder 200 may signal motion vectors in a predictive manner. Two examples of predictive signaling techniques that may be implemented by the video encoder 200 include advanced motion vector prediction (AMVP) and Merge mode signaling.

[0077] The intra prediction unit 206 may perform intra prediction on the current video block. When the intra prediction unit 206 performs intra prediction on the current video block, the intra prediction unit 206 may generate prediction data for the current video block based on the decoded samples of other video blocks in the same picture. The prediction data for the current video block may include a predicted video block and various syntax elements.

[0078] The residual generation unit 207 may generate residual data for the current video block by subtracting (e.g., indicated by a minus sign) the (multiple) predicted video blocks of the current video block from the current video block. The residual data of the current video block may include residual video blocks corresponding to different sample components of the samples in the current video block.

[0079] In other examples, such as in the skip mode, there may be no residual data for the current video block, and the residual generation unit 207 may not perform the subtraction operation.

[0080] The transform processing unit 208 may generate one or more transform coefficient video blocks for the current video block by applying one or more transforms to the residual video block associated with the current video block.

[0081] After the transform processing unit 208 generates the transform coefficient video block associated with the current video block, the quantization unit 209 may quantize the transform coefficient video block associated with the current video block based on one or more quantization parameter (QP) values associated with the current video block.

[0082] The inverse quantization unit 210 and the inverse transform unit 211 can apply inverse quantization and inverse transform to the transformed coefficient video block respectively to reconstruct the residual video block from the transformed coefficient video block. The reconstruction unit 212 can add the reconstructed residual video block to the corresponding samples of one or more predicted video blocks generated by the prediction unit 202 to generate a reconstructed video block associated with the current video block for storage in the buffer 213.

[0083] After the reconstruction unit 212 reconstructs the video block, a loop filtering operation can be performed to reduce the block effect artifacts in the video block.

[0084] The entropy encoding unit 214 can receive data from other functional components of the video encoder 200. When the entropy encoding unit 214 receives the data, the entropy encoding unit 214 can perform one or more entropy encoding operations to generate the entropy encoded data and output a bitstream including the entropy encoded data.

[0085] Figure 3 is a block diagram showing an example of a video decoder 300 according to some embodiments of the present disclosure. The video decoder 300 can be Figure 1 an example of the video decoder 124 in the system 100 shown.

[0086] The video decoder 300 can be configured to perform any or all of the techniques of the present disclosure. In Figure 3 the example, the video decoder 300 includes a plurality of functional components. The techniques described in the present disclosure can be shared among the various components of the video decoder 300. In some examples, a processor can be configured to perform any or all of the techniques described in the present disclosure.

[0087] In Figure 3 the example, the video decoder 300 includes an entropy decoding unit 301, a motion compensation unit 302, an intra prediction unit 303, an inverse quantization unit 304, an inverse transform unit 305, and a reconstruction unit 306 and a buffer 307. In some examples, the video decoder 300 can perform a decoding process generally opposite to the encoding process described with respect to the video encoder 200.

[0088] The entropy decoding unit 301 can retrieve the encoded bitstream. The encoded bitstream can include entropy-encoded video data (e.g., encoded blocks of video data). The entropy decoding unit 301 can decode the entropy-encoded video data, and the motion compensation unit 302 can determine motion information from the entropy-decoded video data, which includes motion vectors, motion vector precision, reference picture list indices, and other motion information. The motion compensation unit 302 can determine such information, for example, by performing AMVP and Merge mode. AMVP is used, including deriving several most likely candidates based on data from adjacent PBs and reference pictures. Motion information generally includes horizontal motion vector displacement values and vertical motion vector displacement values, one or two reference picture indices, and in the case of a prediction region in a B slice, also an indication of which reference picture list is associated with each index. As used herein, in some aspects, "Merge mode" can refer to deriving motion information from spatially adjacent blocks or temporally adjacent blocks.

[0089] The motion compensation unit 302 can generate motion-compensated blocks, possibly performing interpolation based on an interpolation filter. An identifier for the interpolation filter used at sub-pixel precision can be included in the syntax element.

[0090] The motion compensation unit 302 can use the interpolation filter used by the video encoder 200 during the encoding of the video block to calculate the interpolated values for sub-integer pixels of the reference block. The motion compensation unit 302 can determine the interpolation filter used by the video encoder 200 based on the received syntax information, and the motion compensation unit 302 can use the interpolation filter to generate a prediction block.

[0091] The motion compensation unit 302 can use at least part of the syntax information to determine the size of the blocks for encoding the (multiple) frames and / or (multiple) slices of the encoded video sequence, the partitioning information describing how each macroblock of the pictures of the encoded video sequence is partitioned, the mode indicating how each partition is encoded, one or more reference frames (and reference frame lists) for each inter-frame encoded block, and other information for decoding the encoded video sequence. As used herein, in some aspects, a "slice" can refer to a data structure that can be decoded independently of other slices of the same picture in terms of entropy encoding / decoding, signal prediction, and residual signal reconstruction. A slice can be the entire picture or can also be a region of the picture.

[0092] The intra prediction unit 303 can use, for example, the intra prediction mode received in the bitstream to form a prediction block from spatially adjacent blocks. The inverse quantization unit 304 inverse quantizes (i.e., dequantizes) the quantized video block coefficients provided in the bitstream and decoded by the entropy decoding unit 301. The inverse transform unit 305 applies an inverse transform.

[0093] The reconstruction unit 306 can obtain the decoded block by, for example, adding the residual block to the corresponding prediction block generated by the motion compensation unit 302 or the intra prediction unit 303. If necessary, a deblocking filter can also be applied to filter the decoded block to remove blocking artifacts. The decoded video block is then stored in the buffer 307, which provides reference blocks for subsequent motion compensation / intra prediction, and the buffer 307 also generates the decoded video for presentation on a display device. Some exemplary embodiments of the present disclosure will be described in detail below. It should be noted that the use of section headings in this document is for ease of understanding and does not limit the embodiments disclosed in the section to that section. In addition, although some embodiments are described with reference to general video coding / decoding or other specific video codecs, the disclosed techniques are also applicable to other video coding / decoding techniques. In addition, although some embodiments describe the video encoding steps in detail, it should be understood that the corresponding decoding steps for decoding will be implemented by the decoder. In addition, the term video processing includes video encoding or compression, video decoding or decompression, and video transcoding, in which video pixels are represented from one compression format to another or at different compression bitrates. 1. Brief Overview The present disclosure relates to video coding / decoding techniques. Specifically, it relates to the convolutional cross-component model (CCCM), whether to use and how to use the gradients in the CCCM model, and other coding / decoding tools in image / video coding / decoding. The technique can be applied to existing video coding / decoding standards such as HEVC or versatile video coding (VVC). It can also be applicable to future video coding / decoding standards or video codecs. 2. Introduction Video coding standards have evolved mainly through the development of well-known ITU-T and ISO / IEC standards. H.261 and H.263 produced by ITU-T, MPEG-1 and MPEG-4 Visual produced by ISO / IEC, and H.262 / MPEG-2 video, H.264 / MPEG-4 Advanced Video Coding (AVC), and H.265 / HEVC standards jointly produced by the two organizations. Since H262, video coding standards have been based on a hybrid video coding structure that utilizes temporal prediction plus transform coding. To explore future video coding technologies beyond HEVC, the Joint Video Exploration Team (JVET) was jointly established by VCEG and MPEG in 2015. Since then, JVET has adopted many new methods and incorporated them into a reference software called the Joint Exploration Model (JEM). In April 2018, the Joint Video Experts Team (JVET) between VCEG (Q6 / 16) and ISO / IEC JTC1 SC29 / WG11 (MPEG) was created with the goal of the VVC standard with a 50% bitrate reduction compared to HEVC. 2.1. Color Space and Chroma Subsampling A color space (also known as a color model (or color system)) is an abstract mathematical model that simply describes a range of colors as a tuple of numerical values, typically 3 or 4 values or color components (e.g., RGB). Basically, a color space is an extension of a coordinate system and subspace. For video compression, the most frequently used color spaces are YCbCr and RGB. YCbCr, Y’CbCr, or Y Pb / Cb Pr / Cr (also written as YCBCR or Y’CBCR) is a family of color spaces used as part of the color image pipeline in video and digital photography systems. Y′ is the luminance component, and CB and CR are the blue-difference chrominance component and red-difference chrominance component. Y’ (with the prime) is distinguished from Y, which is luminance, meaning that the light intensity is non-linearly encoded based on gamma-corrected RGB primary colors. Chroma subsampling is the practice of encoding an image by encoding the chrominance information at a lower resolution than the luminance information, taking advantage of the fact that the human visual system has lower sharpness for color differences than for luminance. 2.1.1. 4:4:4 Each of the three Y’CbCr components has the same sampling rate, so there is no chroma subsampling. This scheme is sometimes used in high-end film scanners and film post-production. 2.1.2. 4:2:2 The two chrominance components are sampled at half the sampling rate of luminance: the horizontal chrominance resolution is halved while the vertical chrominance resolution remains the same. This reduces the bandwidth of the signal of the uncompressed video by one third with little visual difference. Examples of the nominal vertical and horizontal positions of the 4:2:2 color format are depicted in Figure 4 the VVC working draft. 2.1.3.4:2:0 In 4:2:0, the horizontal sampling is doubled compared to 4:1:1, but since the Cb and Cr channels are only sampled on every alternate line in this scheme, the vertical resolution is halved. Thus, the data rate is the same. Cb and Cr are each subsampled by a factor of 2 both horizontally and vertically. There are three variants of the 4:2:0 scheme with different horizontal and vertical positioning. · In MPEG-2, Cb and Cr are horizontally co-located. Cb and Cr (interleaved) are located between pixels in the vertical direction. · In JPEG / JFIF, H.261, and MPEG-1, Cb and Cr are arranged in an interleaved fashion, in the middle between alternate luminance samples. · In 4:2:0, DV, Cb, and Cr are horizontally co-located. In the vertical direction, they are co-located on alternate lines. Table 2-1 SubWidthC values and SubHeightC values derived from chroma_format_idc and separate_colour_plane_flag 2.2. Encoding and decoding processes of typical video codecs Figure 5 An example of the encoder block diagram of VVC is shown, which includes three loop filter blocks: the deblocking filter (DF), sample adaptive offset (SAO), and ALF. Different from the DF that uses predefined filters, SAO and ALF utilize the original samples of the current picture to reduce the mean squared error between the original samples and the reconstructed samples, by adding offsets and by applying finite impulse response (FIR) filters respectively, transmitting the offset and filter coefficients through the coding side information. ALF is in the last processing stage of each picture and can be regarded as a tool to try to capture and fix the artifacts created by the previous stages. 2.3. Intra-mode encoding and decoding with 67 intra prediction modes To capture any edge direction presented in natural videos, the number of directional intra modes is extended from 33 to 65, as used in HEVC, as Figure 6As shown, the planar and DC modes remain the same. These more dense direction intra prediction modes apply to all block sizes and for both luma and chroma intra prediction. In HEVC, each intra-coded block has a square shape and the length of each side is a power of 2. Therefore, no division operation is required to generate intra prediction values using the DC mode. In VVC, in general, blocks can have a rectangular shape and division operations need to be used for each block in general. To avoid division operations for DC prediction, only the longer side is used to calculate the average value for non-square blocks. 2.3.1. Wide-angle intra prediction Although 67 modes are defined in VVC, the exact prediction direction for a given intra prediction mode index also depends on the block shape. The conventional angular intra prediction directions are defined as from 45 degrees to 135 degrees in the clockwise direction. In VVC, for non-square blocks, several conventional angular intra prediction modes are adaptively replaced by wide-angle intra prediction modes. The replaced modes are signaled using the original mode index, which is remapped to the index of the wide-angle mode after parsing. The total number of intra prediction modes remains the same, i.e., 67, and the intra mode coding method remains the same. To support these prediction directions, as Figure 7 shown, an upper reference of length 2W+1 and a left reference of length 2H+1 are defined. The number of replaced modes in the wide-angle direction mode depends on the aspect ratio of the block. The replaced intra prediction modes are shown in Table 2-2. Table 2-2 Intra prediction modes replaced by wide-angle modes As Figure 8 shown, in the case of wide-angle intra prediction, two vertically adjacent prediction samples can use two non-adjacent reference samples. Therefore, a low-pass reference sample filter and side smoothing are applied to wide-angle prediction to reduce the negative impact brought by the increased interval Δp α If the wide-angle mode represents a non-fractional offset. There are 8 modes in the wide-angle mode that satisfy this condition, and these modes are [-14, -12, -10, -6, 72, 76, 78, 80]. When predicting a block through these modes, the samples in the reference buffer are directly copied without applying any interpolation. By this modification, the number of samples that need to be smoothed is reduced. In addition, this method also aligns the design of traditional prediction modes with non-fractional modes in wide-angle modes. In VVC, 4:2:2 and 4:4:4 chroma formats as well as 4:2:0 chroma format are supported. The derivation table for the chroma derivation mode (DM) for the 4:2:2 chroma format was initially derived from HEVC, and the number of entries was extended from 35 to 67 to align with the extension of the intra prediction mode. Since the HEVC specification does not support prediction angles below -135 degrees and above 45 degrees, the luma intra prediction modes in the range from 2 to 5 are mapped to 2. Therefore, the chroma DM derivation table for the 4:2:2 chroma format is updated by replacing some values of the mapping table entries to more accurately transform the prediction angles for chroma blocks. 2.4. Intra Prediction Mode Coding and Decoding for Chrominance Components For the chrominance components of an intra PU, the encoder selects the best chroma prediction mode among five modes, which include planar, DC, horizontal, vertical, and direct copy of the intra prediction mode for the luma component. The mapping between the intra prediction direction for chroma and the intra prediction mode number is shown in Table 2-3. When the intra prediction mode number for the chrominance component is 4, the intra prediction direction for the luma component is used for generating the intra prediction samples for the chrominance component. When the intra prediction mode number for the chrominance component is not 4 and is the same as the intra prediction mode number for the luma component, the intra prediction direction 66 is used for generating the intra prediction samples for the chrominance component. 2.5. Inter Prediction For each inter-predicted coding unit (CU), the motion parameters include motion vectors, reference picture indices, reference picture list use indices, and additional information required for the new decoding features of VVC to be used for generating inter-predicted samples. The motion parameters can be signaled in an explicit or implicit manner. When a CU is coded in skip mode, the CU is associated with a PU and has no significant residual coefficients, no coded motion vector differences, or reference picture indices. A Merge mode is specified in VVC, in which the motion parameters for the current CU are obtained from neighboring CUs, including spatial candidates and temporal candidates, as well as other schedulings introduced in VVC. The Merge mode can be applied to any inter-predicted CU, not only to skip mode. The alternative to the Merge mode is the explicit transmission of motion parameters, in which the motion vectors, the corresponding reference picture indices for each reference picture list and the reference picture list use flags, and other required information are explicitly signaled for each CU. 2.6. Intra Block Copy (IBC) Intra Block Copy (IBC) is a tool adopted in the HEVC extension on SCC. As is well known, it significantly improves the codec efficiency of screen content material. Since the IBC mode is implemented as a block-level codec mode, block matching (BM) is performed at the encoder to find the optimal block vector (or motion vector) for each CU. Here, the block vector is used to indicate the displacement from the current block to the reference block, which has been reconstructed within the current picture. The luminance block vectors of the CUs encoded / decoded by IBC have integer precision. The chrominance block vectors are also rounded to integer precision. When combined with AMVR, the IBC mode can switch between 1-pixel and 4-pixel motion vector precisions. The IBC-encoded / decoded CU is regarded as a third prediction mode in addition to the intra prediction mode or the inter prediction mode. The IBC mode is applicable to CUs with both width and height less than or equal to 64 luminance samples. On the encoder side, hash-based motion estimation is performed for IBC. The encoder performs RD checks for blocks with width or height no greater than 16 luminance samples. For non-Merge modes, block vector search is first performed using hash-based search. If the hash search does not return a valid candidate, block-matching based local search will be performed. In the hash-based search, the hash key match (32-bit CRC) between the current block and the reference block is extended to all allowed block sizes. The calculation of the hash key for each position in the current picture is based on 44 sub-blocks. For a larger-sized current block, when all the hash keys of all 4×4 sub-blocks match the hash keys in the corresponding reference positions, it is determined that the hash key matches the hash key of the reference block. If multiple reference block hash keys are found to match the current block's hash key, the block vector cost of each matched reference is calculated, and the one with the minimum cost is selected. In the block-matching search, the search range is set to cover both the previous CTU and the current CTU. At the CU level, the IBC mode uses flags to signal, and it can be signaled as the following IBC AMVP mode or IBC Skip / Merge mode: - IBC Skip / Merge mode: The Merge candidate index is used to indicate which block vectors from the list of neighboring candidate IBC-encoded blocks are used to predict the current block. The Merge list consists of spatial neighbors, HMVP, and paired candidates. - IBC AMVP mode: The block vector difference is encoded / decoded in the same way as the motion vector difference. The block vector prediction method uses two candidates as prediction values, one from the left neighbor and one from the upper neighbor (if IBC-encoded / decoded). When either neighbor is unavailable, the default block vector is used as the prediction value. A flag is signaled to indicate the block vector prediction value index. 2.7. Cross-component linear model prediction To reduce cross-component redundancy, the cross-component linear model (CCLM) prediction mode is used in VVC, where chroma samples are predicted based on the reconstructed luma samples of the same CU, and a linear model as shown below is used: pred C (i, j) = α · rec L ′(i, j) + β (2-1) where pred C (i,j) represents the predicted chroma sample in the CU, and rec L (i,j) represents the downsampled reconstructed luma sample of the same CU. The CCLM parameters (α and β) are derived using up to four neighboring chroma samples and their corresponding downsampled luma samples. Assuming the current chroma block dimensions are W×H, W’ and H’ are set to – When the LM mode is applied, W’ = W, H’ = H; – When the LM_T mode is applied, W’ = W + H; – When the LM_L mode is applied, H’ = H + W. The upper neighboring positions are denoted as S[0, -1]…S[W’-1, -1], and the left neighboring positions are denoted as S[-1, 0]…S[-1, H’-1]. Then four samples are selected as – When the LM mode is applied and both the upper neighboring sample and the left neighboring sample are available, select S[W’ / 4, -1], S[3*W’ / 4, -1], S[-1, H’ / 4], S[-1, 3*H’ / 4] – When the LM_T mode is applied, or only the upper neighboring sample is available, select S[W’ / 8, -1], S[3*W’ / 8, -1], S[5*W’ / 8, -1], S[7*W’ / 8, -1]; – When the LM_L mode is applied, or only the left neighboring sample is available, select S[-1, H’ / 8], S[-1, 3*H’ / 8], S[-1, 5*H’ / 8], S[-1, 7*H’ / 8]. The four neighboring luma samples at the selected positions are downsampled, and four comparisons are made to find two larger values: x 0 A and x 1 A , and two smaller values: x 0 B and x 1 B . The corresponding chroma sample values are denoted as y 0A , y 1 A , y 0 B and y 1 B . Then, x A , x B , y A and y B The derivation of and y is as follows: X a = (x 0 A + x 1 A + 1) >> 1; X b = (x 0 B + x 1 B + 1) >> 1; Y a = (y 0 A + y 1 A + 1) >> 1; Y b = (y 0 B + y 1 B + 1) >> 1(2 - 2) Finally, the linear model parameters α and β are obtained according to the following equations. β = Y b - α · X b (2 - 4) Figure 9 Shows an example of the positions of the left sample point and the upper sample point involved in the CCLM mode and the sample points of the current block. The division operation for calculating the parameter α is implemented through a lookup table. To reduce the memory required to store the table, the diff value (the difference between the maximum and minimum values) and the parameter α are represented by exponential notation. For example, diff is approximated with 4 bits of significant part and an exponent. Thus, the lookup table for 1 / diff is reduced to 16 elements for 16 valid values as follows: DivTable[] = {0, 7, 6, 5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 0} (2 - 5) This not only helps to reduce the computational complexity but also decreases the memory size required to store the lookup table. In addition to the upper template and the left template being used together to calculate the linear model coefficients, they can alternatively be used in other 2LM modes, called the LM_T and LM_L modes. In the LM_T mode, only the upper template is used to calculate the linear model coefficients. To obtain more samples, the upper template is extended to (W + H) samples. In the LM_L mode, only the left template is used to calculate the linear model coefficients. To obtain more samples, the left template is extended to (H + W) samples. In the LM mode, both the left and upper templates are used to calculate the linear model coefficients. To match the chrominance sample positions for 4:2:0 video sequences, two types of downsampling filters are applied to the luma samples to achieve a 2-to-1 downsampling ratio in both the horizontal and vertical directions. The selection of the downsampling filter is specified by the SPS level flag. The two downsampling filters are as follows, corresponding to "type 0" and "type 2" content respectively. Note that when the upper reference line is at the CTU boundary, only one luma line (the general line buffer in intra prediction) is used for the downsampled luma samples. This parameter calculation is performed as part of the decoding process and not just an encoder search operation. As a result, no syntax is used to convey the α and β values to the decoder. For chrominance intra mode coding and decoding, a total of 8 intra modes are allowed for chrominance intra mode coding and decoding. These modes include five regular intra modes and three cross-component linear model modes (LM, LM_T, and LM_L). The chrominance mode signaling and derivation process are shown in Table 2-3. Chrominance mode coding and decoding directly depend on the intra prediction mode of the corresponding luma block. Since a separate block partition structure for both luma and chrominance components is enabled in I slices, one chrominance block can correspond to multiple luma blocks. Therefore, for the chrominance DM mode, the intra prediction mode of the corresponding luma block covering the center position of the current chrominance block is directly inherited. Table 2-3 Deriving Chrominance Prediction Modes from Luma Modes when CCLM is Enabled Regardless of the value of sps_cclm_enabled_flag, a single binarization table is used, as shown in Table 2-4. Table 2-4 Unified Binarization Table for Chrominance Prediction Modes In Table 2-4, the first binary bit indicates whether it is the normal mode (0) or the LM mode (1). If it is the LM mode, then the next binary bit indicates whether it is LM_CHROMA (0). If it is not LM_CHROMA, then the next binary bit indicates whether it is LM_L (0) or LM_T (1). For this case, when the sps_cclm_enabled_flag is 0, the first binary bit in the binarization table for the corresponding intra_chroma_pred_mode can be discarded before entropy coding and decoding. Or, in other words, the first binary bit is presumed to be 0 and thus not coded or decoded. This single binarization table is used for both cases where the sps_cclm_enabled_flag is equal to 0 and 1. The first two binary bits in Table 2-4 are context-coded using their respective context models, and the remaining binary bits are bypass-coded. In addition, to reduce the luminance-chrominance delay in the dual-tree, when the 64×64 luminance coding and decoding tree node is not partitioned (and the ISP is not used for the 64×64 CU) or the QT is split, the chrominance CUs in the chrominance coding and decoding tree nodes in 32×32 / 32×16 can use CCLM in the following way: – If the 32×32 chrominance node is not partitioned or is split by QT partitioning, then all chrominance CUs in this 32×32 node can use CCLM; – If the 32×32 chrominance node is split by horizontal BT and the 32×16 child node is not partitioned or is split by vertical BT partitioning, then all chrominance CUs in this 32×16 chrominance node can use CCLM. Under all other luminance and chrominance coding and decoding tree partitioning conditions, CCLM is not allowed for chrominance CUs. 2.8. Multiple Model Linear Model (MMLM) With MMLM, there can be more than one linear model between the luminance samples and the chrominance samples in a CU. In this method, the neighboring luminance samples and neighboring chrominance samples of the current block are classified into multiple groups, and each group is used as a training set to derive a linear model (i.e., specific α and β are derived for a specific group). In addition, the samples of the current luminance block are also classified based on the same rules as the classification of the neighboring luminance samples. The neighboring samples can be classified into M groups, where M is 2 or 3. In addition to the original LM mode, the MMLM methods with M = 2 and M = 3 are respectively designed as two new chrominance prediction modes, named MMLM2 and MMLM3. The encoder selects the optimal mode during the RDO process and signals this mode. When M is equal to 2, Figure 10An example of classifying neighboring samples into two groups is shown. The threshold is calculated as the average of neighboring reconstructed luminance samples. Neighboring samples with Rec’L[x,y]<=Threshold are classified into group 1; while neighboring samples with Rec′ L [x,y]>Threshold are classified into group 2. Similar to CCLM, there are 3 modes in MMLM, namely MMLM, MMLM_T, and MMLM_L. The two models are derived as follows: The threshold is the average of luminance reconstructed neighboring samples. The linear model for each category is derived using the least mean square (LMS) method (if enabled) or the minimum / maximum method of VVC. 2.9. Position-dependent Intra Prediction Combination In VVC, the intra prediction results of DC, planar, and several angular modes can also be corrected by the position-dependent intra prediction combination (PDPC) method. PDPC is an intra prediction method that utilizes filtered boundary reference samples to call the combination of boundary reference samples and HEVC-style intra prediction. PDPC is applied to the following intra modes without signaling: planar, DC, intra angles less than or equal to horizontal, and intra angles greater than or equal to vertical and less than or equal to 80. If the current block is in BDPCM mode or the MRL index is greater than 0, PDPC is not applied. According to Equation 2-8, the predicted sample pred(x’,y’) is predicted using a linear combination of the intra prediction mode (DC, planar, angular) and reference samples as follows: pred(x’,y’)=Clip(0,(1<<BitDepth)-1,(wL×R -1，y’ +wT×R x’，-1 +(64-wL-wT)×pred(r',y')+32)>>6) (2-9) where R x,-1 ,R -1,y represent the reference samples located at the upper boundary and left boundary of the current sample (x, y), respectively. If PDPC is applied to DC, planar, horizontal, and vertical intra modes, no additional boundary filters are required, as required in the case of HEVC DC mode boundary filters or horizontal / vertical mode edge filters. The PDPC process for DC mode and planar mode is the same. For angular modes, if the current angular mode is HOR_IDX or VER_IDX, the left reference sample or the upper reference sample is not used, respectively. The PDPC weights and scaling factors depend on the prediction mode and block size. PDPC is applied to blocks with a width and height greater than or equal to 4. Figures 11A to 11D shows the definition of reference samples (R x,-1 and R -1,y ) for PDPC applied in various prediction modes. The prediction sample pred(x’, y’) is located at (x’, y’) within the prediction block. As an example, for the diagonal mode, the x coordinate of the reference sample R x,-1 is given by: x = x’ + y’ + 1, and the y coordinate of the reference sample R -1,y is similarly given by y = x’ + y’ + 1. For another angular mode, the reference samples R x,-1 and R -1,y can be located at fractional sample positions. In this case, the sample value at the nearest integer sample position is used. 2.10. Gradient PDPC The gradient-based approach is extended for non-vertical / non-horizontal modes as Figure 12 shown. Here, the gradient is calculated as r(-1, y) - r(-1 + d, -1), where d is the horizontal displacement depending on the angular direction. Several points to note here: The gradient term r(-1, y) - r(-1 + d, -1) needs to be calculated once per row as it does not depend on the x position. The calculation of d is already part of the original intra-frame prediction process and can be reused, so d does not need to be calculated separately. Thus, d is at 1 / 32 pixel precision. When d is at a fractional position, two-tap (linear) filtering is already used, i.e., if dPos is the displacement in 1 / 32 pixel precision, dInt is the (rounded down) integer part (dPos >> 5), and dFrat is the fractional part in 1 / 32 pixel precision (dPos & 31), then r(-1 + d) is calculated as: r(-1 + d) = (32 – dFrac)*r(-1 + dInt) + dFrac*r(-1 + dInt + 1) This two-tap filtering is performed once per row (if needed) as explained in a. Finally, the prediction signal is calculated. p(x, y) = Clip(((64 - wL(x))*p(x, y) + wL(x)*(r(-1, y) - r(-1 + d, -1)) + 32) >> 6) where wL(x) = 32 >> ((x << 1) >> nScale2), and nScale2 = (log2(nTbH) + log2(nTbW) – 2) >> 2, which is the same as for the vertical / horizontal mode. In short, the same process is applied as for the vertical / horizontal mode (in fact, d = 0 indicates the vertical / horizontal mode). Second, when (n Scale < 0) or when the PDPC cannot be applied due to the unavailability of the secondary reference sample points, activate the gradient-based approach for non-vertical / non-horizontal modes. We have shown the value of nScale in Figure 13 with respect to the TB size and the angular mode to better visualize the case of using the gradient approach. Additionally, in Figure 14 , we have shown the flowcharts for the current and the proposed PDPC. Figure 13 The value of nScale in Figure 13 has been shown with respect to the TB size and the angular mode to better visualize the case of using the gradient approach. Additionally, in Figure 14 , we have shown the flowcharts for the current and the proposed PDPC. Figure 14 The value of nScale in Figure 13 has been shown with respect to the TB size and the angular mode to better visualize the case of using the gradient approach. Additionally, in Figure 14 , we have shown the flowcharts for the current and the proposed PDPC. 2.11. Secondary MPM The existing primary MPM (PMPM) list consists of 6 entries, and the secondary MPM (SMPM) list includes 16 entries. First, a general MPM list with 22 entries is constructed, and then the first 6 entries in the general MPM list are included in the PMPM list, and the remaining entries form the SMPM list. The first entry in the general MPM list is the planar mode. The remaining entries consist of the intra modes of the left (L), above (A), bottom-left (BL), top-right (AR), and top-left (AL) neighboring blocks, as shown in Figure 15 , with the directional mode having an increased offset from the first two available directional modes of the neighboring blocks, and the default mode. Figure 15 The existing primary MPM (PMPM) list consists of 6 entries, and the secondary MPM (SMPM) list includes 16 entries. First, a general MPM list with 22 entries is constructed, and then the first 6 entries in the general MPM list are included in the PMPM list, and the remaining entries form the SMPM list. The first entry in the general MPM list is the planar mode. The remaining entries consist of the intra modes of the left (L), above (A), bottom-left (BL), top-right (AR), and top-left (AL) neighboring blocks, as shown in Figure 15 , with the directional mode having an increased offset from the first two available directional modes of the neighboring blocks, and the default mode. If the CU block is vertically oriented, the order of the neighboring blocks is A, L, BL, AR, AL; otherwise, the order is L, A, BL, AR, AL. First, parse the PMPM flag. If it is equal to 1, parse the PMPM index to determine which entry in the PMPM list is selected; otherwise, parse the SPMPM flag to determine whether to parse the SMPM index or the remaining mode. 2.12. 6-Tap Intra Interpolation Filter To improve the prediction accuracy, it is proposed to use a 6-tap interpolation filter instead of a 4-tap cubic interpolation filter. The filter coefficients are derived based on the same polynomial regression model, except that the polynomial order is 6. The filter coefficients are listed below, {0, 0, 256, 0, 0, 0}, / / 0 / 32 position {0, -4, 253, 9, -2, 0}, / / 1 / 32 position {1, -7, 249, 17, -4, 0}, / / 2 / 3 position {1, -10, 245, 25, -6, 1}, / / 3 / 32 position {1, -13, 241, 34, -8, 1}, / / 4 / 32 position {2, -16, 235, 44, -10, 1}, / / 5 / 32 position {2, -18, 229, 53, -12, 2}, / / 6 / 32 position {2, -20, 223, 63, -14, 2}, / / 7 / 32 position {2, -22, 217, 72, -15, 2}, / / 8 / 32 position {3, -23, 209, 82, -17, 2}, / / 9 / 32 position {3, -24, 202, 92, -19, 2}, / / 10 / 32 position {3, -25, 194, 101, -20, 3}, / / 11 / 32 position {3, -25, 185, 111, -21, 3}, / / 12 / 32 position {3, -26, 178, 121, -23, 3}, / / 13 / 32 position {3, -25, 168, 131, -24, 3}, / / 14 / 32 position {3, -25, 159, 141, -25, 3}, / / 15 / 32 position {3, -25, 150, 150, -25, 3}, / / Half - pixel position The reference samples for interpolation are taken from the reconstructed samples or filled as in HEVC, such that no conditional check for reference sample availability is required. Instead of using the round - off operation to derive the extended intra - reference samples, a 4 - tap cubic interpolation filter is proposed. As Figure 16 shown in the example in, to derive the value of reference sample P, a four - tap interpolation filter is used, while in JEM - 3.0 or HM, P is directly set to X1. 2.13. Multiple - reference - line (MRL) intra prediction Multiple - reference - line (MRL) intra prediction uses more reference lines for intra prediction. In Figure 17 , an example of 4 reference lines is depicted, where the samples in segments A and F are not obtained from the reconstructed neighboring samples, but are filled with the nearest samples from segments B and E respectively. HEVC intra - picture prediction uses the nearest reference line (i.e., reference line 0). In MRL, 2 additional lines (reference line 1 and reference line 2) are used. The index of the selected reference line (mrl_idx) is signaled and used to generate the intra - prediction value. For reference line indices greater than 0, only the additional reference - line modes are included in the MPM list, and only the MPM index is signaled, while the remaining modes are signaled. The reference line index is signaled before the intra - prediction mode, and in the case of signaling a non - zero reference line index, the planar mode is excluded from the intra - prediction mode. Disable MRL for the first row of blocks within a CTU to prevent the use of extended reference samples outside the current CTU row. Additionally, PDPC is disabled when additional rows are used. For the MRL mode, the derivation of the DC value in the DC intra prediction mode for non-zero reference row indices is aligned with the derivation for reference row index 0. MRL requires using the CTU to store 3 neighboring luma reference rows to generate predictions. The Cross-Component Linear Model (CCLM) tool also requires 3 neighboring luma reference rows for its downsampling filter. The definition of MRL using the same 3 rows is aligned with CCLM to reduce the storage requirements for the decoder. 2.14. Intra Sub-Partitioning (ISP) Intra Sub-Partitioning (ISP) vertically or horizontally divides the luma intra prediction block into 2 or 4 sub-partitions according to the block size. For example, the minimum block size for ISP is 4×8 (or 8×4). If the block size is greater than 4×8 (or 8×4), then the corresponding block is divided by 4 sub-partitions. Note that ISP blocks of M×128 (M≤64) and 128×N (N≤64) may pose potential problems for the 64×64 VDPU. For example, in the case of a single tree, a CU of M×128 has an M×128 luma TB and two corresponding chroma TBs. If the CU uses ISP, the luma TB will be divided into four M×32 TBs (only horizontal division is possible), each of which is smaller than a 64×64 block. However, in the current design, the ISP chroma blocks are not divided. Therefore, the two chroma components will have a size greater than 32×32 blocks. Similarly, a similar situation can be created using ISP with a 128×N CU. Therefore, these two cases have problems for the 64×64 decoder pipeline. For this reason, the CU size can be limited to a maximum of 64×64 using ISP. Figure 18A and Figure 18B Examples showing two possibilities are presented. All sub-partitions meet the condition of having at least 16 samples. In the ISP, it is not allowed that the 1×N / 2×N sub-block prediction depends on the reconstructed values of the previously decoded 1×N / 2×N sub-blocks in the coded block, so that the minimum width of the prediction for the sub-block becomes four samples. For example, for an 8×N (N>4) coded block coded using an ISP with vertical partitioning, it is divided into two prediction regions of size 4×N and four transforms of size 2×N. In addition, for a 4×N coded block coded using an ISP with vertical partitioning, it is predicted using the entire 4×N block; four transforms of size 1×N are used. Although the transform sizes of 1×N and 2×N are allowed, it is confirmed that the transforms of the blocks located in the 4×N region can be executed in parallel. For example, when the 4×N prediction region contains four 1×N transforms, there is no transform in the horizontal direction; the transforms in the vertical direction can be executed as a single 4×N transform in the vertical direction. Similarly, when the 4×N prediction region contains two 2×N transform blocks, the transform operations of the two 2×N blocks can be performed in parallel in each direction (horizontal and vertical). Therefore, compared with processing a 4×4 conventional coded intra-block, processing these smaller blocks does not introduce an increased delay. Table 2-5 Entropy Coding / Decoding Coefficient Group Sizes Block size Coefficient group size 1×N, N≥16 1×16 N×1, N≥16 16×1 2×N, N≥8 2×8 N×2, N≥8 8×2 All other possible M×N cases 4×4 For each sub-partition, the reconstructed samples are obtained by adding the residual signal to the prediction signal. Here, the residual signal is generated by processes such as entropy decoding, inverse quantization, and inverse transformation. Therefore, the reconstructed sample values of each sub-partition can be used to generate the prediction for the next sub-partition, and each sub-partition is processed repeatedly. Additionally, the first sub-partition to be processed is a sub-partition containing the top-left sample of the CU, and then continues down (horizontal partitioning) or to the right (vertical partitioning). Therefore, the reference samples used to generate the sub-partition prediction signal are only located on the left and above the row. All sub-partitions share the same intra-mode. The following is an overview of the interaction between the ISP and other coding / decoding tools. – Multiple Reference Lines (MRL): If the block has a non-zero MRL index, then it is presumed that the ISP coding / decoding mode is 0 and thus the ISP mode information will not be sent to the decoder. – Entropy Coding / Decoding Coefficient Group Sizes: The sizes of the entropy coding / decoding sub-blocks have been modified so that they have 16 samples in all possible cases, as shown in Table 2-5. Note that the new sizes only affect the blocks generated by the ISP where one dimension is less than 4 samples. In all other cases, the coefficient group remains 4×4 dimension. – CBF Coding / Decoding: Assume that at least one sub-partition has a non-zero CBF. Therefore, if the number of sub-partitions n and the first n - 1 sub-partitions have produced zero CBF, then the CBF of the nth sub-partition is presumed to be 1. – Transform Size Limitation: All ISP transforms with a length greater than 16 points use DCT-II. – MTS flag: If the CU uses the ISP coding / decoding mode, the MTS CU flag will be set to 0 and it will not be sent to the decoder. Thus, the encoder will not perform RD tests for different available transforms for each resulting sub - partition. Instead, the transform selection for the ISP mode will be fixed and selected according to the intra - mode used, the processing order, and the block size. Thus, no signaling is required. For example, let t H and t V be the horizontal transform and the vertical transform selected for the w×h sub - partition respectively, where w is the width and h is the height. Then the transform is selected according to the following rules: – If w = 1 or h = 1, then the horizontal or vertical transform is not performed respectively. – If w ≥ 4 and w ≤ 16, then t H = DST - VII; otherwise, t H = DCT - II – If h ≥ 4 and h ≤ 16, then t V = DST - VII; otherwise, t V = DCT - II In the ISP mode, all 67 intra - prediction modes are allowed. If the corresponding width and height are at least 4 samples long, PDPC is also applied. Additionally, there is no longer a reference sample filtering process (reference smoothing) and the conditions for the selection of the intra - interpolation filter, and the cubic (DCT - IF) filter is always applied to the fractional - position interpolation in the ISP mode. 2.15. Matrix - weighted Intra - prediction (MIP) The matrix - weighted intra - prediction (MIP) method is an intra - prediction technique newly added to the VVC. To predict the samples of a rectangular block of width W and height H, the matrix - weighted intra - prediction (MIP) takes as input a row of H reconstructed neighboring boundary samples to the left of the block and a row of W reconstructed neighboring boundary samples above the block. If the reconstructed samples are not available, they are generated in the same way as in the conventional intra - prediction. The generation of the prediction signal is based on the following three steps, which are averaging, matrix - vector multiplication, and linear interpolation, as Figure 19 shown. 2.15.1. Averaging neighboring samples Among the boundary samples, four samples or eight samples are selected by averaging based on the block size and shape. Specifically, by averaging the neighboring boundary samples according to a predefined rule and in accordance with the block size, the input boundaries bdry top and bdry left are reduced to smaller boundaries and Then, the two reduced boundaries and is spliced to the reduced boundary vector bdry red , the reduced boundary vector thus having a size of 4 for the shape 4×4 block and a size of 8 for all other shaped blocks. If the mode refers to the MIP mode, the splicing is defined as follows: 2.15.2 Matrix Multiplication Perform matrix-vector multiplication using the average samples as input, and then add an offset. The result is a reduced prediction signal on a subsampled set of samples in the original block. Outside the reduced input vector bdry red , generate the reduced prediction signal pred red , the reduced prediction signal being a signal on a downsampled block of width W red and height H red . Here, W red and H red are defined as: Calculate the reduced prediction signal pred red by computing the matrix-vector product and adding an offset: pred red = A·bdry red + b (2-13) Here, A is a matrix, the matrix A having W red ·H red rows, the matrix having 4 columns when W = H = 4, and having 8 columns in all other cases. b is a vector of size W red ·H red . The matrix A and the offset vector b are taken from one of the sets S0, S1, S2, and define the index idx = idx(W,H) as follows: Here, each coefficient of the matrix A is represented with 8-bit precision. The set S0 consists of 16 matrices each having 16 rows and 4 columns and 16 offset vectors each offset vector being of size 16. The matrices and offset vectors of this set are used for blocks of size 4×4. The set S1 consists of 8 matrices each having 16 rows and 8 columns and 8 offset vectors each offset vector being of size 16. The set S2 consists of 6 matrices each having 64 rows and 8 columns and 6 offset vectors of size 64 2.15.3. Interpolation The prediction signal at the remaining positions is generated by linear interpolation from the prediction signals on the sub-sampled set, and the linear interpolation is a one-step linear interpolation in each direction. The interpolation is first performed in the horizontal direction and then in the vertical direction, regardless of the block shape or block size. 2.15.4 Signaling of the MIP mode and coordination with other coding tools For each coding unit (CU) in the intra mode, a flag indicating whether the MIP mode is to be applied is sent. If the MIP mode is to be applied, the MIP mode (predModeIntra) is signaled. For the MIP mode, a transpose flag that determines whether the mode is transposed, and a mode ID (modeId) that determines which matrix is to be used for the given MIP mode are derived as follows: isTransposed = predModeIntra & 1 modeId = predModeIntra >> 1 (2-15) The MIP coding mode is coordinated with other coding tools by considering the following aspects: - LFNST is enabled for MIP on large blocks. Here, the LFNST transform in the planar mode is used. - The derivation of reference samples for MIP is performed as precisely as in the conventional intra prediction mode. - For the upsampling step used in MIP prediction, the original reference samples are used instead of the downsampled samples. - Clipping is performed before upsampling instead of after upsampling. - Without considering the maximum transform size, MIP is allowed to reach 64×64. For sizeId = 0, the number of MIP modes is 32, for sizeId = 1, the number of MIP modes is 16, and for sizeId = 2, the number of MIP modes is 12. 2.16. Intra mode derivation on the decoder side In JEM-2.0, the intra mode is extended from 35 modes in HEVC to 67, and it is derived at the encoder and explicitly signaled to the decoder. A large amount of overhead is spent on intra mode coding and decoding in JEM-2.0. For example, in all intra coding and decoding configurations, the intra mode signaling overhead can be as high as 5% to 10% of the total bit rate. This proposal presents an intra mode derivation method on the decoder side to reduce the intra mode coding and decoding overhead while maintaining the prediction accuracy. To reduce the overhead of intra mode signaling, this proposal presents a decoder-side intra mode derivation (DIMD) approach. In the proposed approach, instead of explicitly signaling the intra mode, information is derived from the neighboring reconstructed samples of the current block at both the encoder and the decoder. The intra mode derived by DIMD is used in two ways: 1) For a 2N×2N CU, when the corresponding CU-level DIMD flag is enabled, the DIMD mode is used as the intra mode for intra prediction; 2) For an N×N CU, the DIMD mode is used to replace one candidate in the existing MPM list to improve the intra mode coding and decoding efficiency. 2.16.1 Template-based intra mode derivation As Figure 20 illustrated, the target represents the current block (of block size N) for the intra prediction mode to be estimated. The template (indicated by the patterned area in Figure 20 ) specifies a set of reconstructed samples for deriving the intra mode. The template size is represented as the number of samples extending to the upper and left sides of the target block within the template, i.e., L. In the current implementation, a template size of 2 (i.e., L = 2) is used for 4×4 and 8×8 blocks, and a template size of 4 (i.e., L = 2) is used for 16×16 and larger blocks. The reference of the template (indicated by the dotted line area in Figure 20 ) refers to a set of neighboring samples from the upper and left sides of the template, as defined in JEM-2.0. Different from the template samples that always come from the reconstructed area, the reference samples of the template may not have been reconstructed when encoding / decoding the target block. In this case, the existing reference sample substitution algorithm of JEM-2.0 is used to substitute the unavailable reference samples with the available reference samples. For each intra prediction mode, DIMD calculates the sum of absolute differences (SAD) between the reconstructed template samples and their predicted samples obtained from the reference samples of the template. The intra prediction mode that produces the minimum SAD is selected as the final intra prediction mode of the target block. 2.16.2 DIMD for intra 2N×2N CUs For intra 2N×2N CUs, DIMD is used as an additional intra mode, which is adaptively selected by comparing the DIMD intra mode with the optimal normal intra mode (i.e., the one explicitly signaled). A flag is signaled for each intra 2N×2N CU to indicate the use of DIMD. If the flag is 1, the intra mode derived by DIMD is used to predict the CU; otherwise, DIMD is not applied, and the intra mode explicitly signaled in the bitstream is used to predict the CU. When DIMD is enabled, the chrominance components always reuse the same intra mode as the intra mode derived for the luminance component (i.e., the DM mode). In addition, for each DIMM-encoded / decoded CU, the blocks in the CU can adaptively select to derive their intra modes at the PU level or the TU level. Specifically, when the DIMD flag is 1, another CU-level DIMD control flag is signaled to indicate the level at which DIMD is performed. If this flag is zero, it means that DIMD is performed at the PU level and all TUs in the PU use the same derived intra mode for their intra prediction; otherwise (i.e., the DIMD control flag is one), it means that DIMD is performed at the TU level and each TU in the PU derives its own intra mode. Furthermore, when DIMD is enabled, the number of angular directions is increased to 129, and the DC and planar modes remain the same. To accommodate the increased granularity of the angular intra modes, the precision of the intra interpolation filtering for the DIMM-encoded / decoded CU is increased from 1 / 32 pixel to 1 / 64 pixel. Additionally, to use the derived intra mode of the DIMM-encoded / decoded CU as an MPM candidate for neighboring intra blocks, those 129 directions of the DIMM-encoded / decoded CU are converted to "normal" intra modes (i.e., 65 angular intra directions) before being used as an MPM. 2.16.3 DIMD for Intra N×N CUs In the proposed method, the intra mode of the intra N×N CU is always signaled. However, to improve the efficiency of intra mode encoding / decoding, the intra mode derived from DIMD is used as an MPM candidate for the intra modes of four PUs in the predicted CU. To not increase the overhead of the MPM index signaling, the DIMD candidate is always placed at the first position in the MPM list and the last existing MPM candidate is removed. In addition, a pruning operation is performed such that if the DIMD candidate is redundant, the DIMD candidate is not added to the MPM list. 2.16.4 Intra Mode Search Algorithm for DIMD To reduce the encoding / decoding complexity, a straightforward fast intra mode search algorithm is used for DIMD. First, an initial estimation process is performed to provide a good starting point for the intra mode search. Specifically, an initial candidate list is created by selecting N fixed modes from the allowed intra modes. Then, the SAD is calculated for all candidate intra modes, and the mode with the minimum SAD is selected as the starting intra mode. To achieve a good complexity / performance trade-off, the initial candidate list consists of 11 intra modes, including DC, planar, and the modes selected every 4 from the 33 angular intra directions defined in HEVC, i.e., intra modes 0, 1, 2, 6, 10…30, 34. If the intra mode in the starting frame is DC or planar, it is used as the DIMD mode. Otherwise, based on the starting intra mode, a refinement process is then applied, where the best intra mode is identified through an iterative search. It works by comparing the SAD values for three intra modes separated by a given search interval at each iteration and maintaining the intra mode that minimizes the SAD. The search interval is then reduced to half, and the selected intra mode from the last iteration is used as the central intra mode for the current iteration. For the current DIMD implementation, up to 4 iterations are used to find the best DIMD intra mode for 129 angular intra directions. 2.17. Decoder-side intra mode derivation by computing gradients of neighboring samples Three angular modes are selected from the gradient histogram (HoG) computed from the neighboring pixels of the current block. Once the three modes are selected, their prediction values are typically computed, and then their weighted average is used as the final prediction value for the block. To determine the weights, the corresponding magnitudes in the HoG are used for each of the three modes. The DIMD mode is used as an alternative prediction mode and is always checked in the FullRD mode. The current version of DIMD has modified some aspects in signaling, HoG computation, and prediction fusion. The purpose of this modification is to improve the codec performance and to address the complexity issues (i.e., the processing volume of 4×4 blocks) raised during the last meeting. The following sections describe the modifications for each aspect. 2.17.1 Signaling Figure 21 Shows the order of parsing flags / indexes in VTM5 integrated with the proposed DIMD. It can be seen that a single CABAC context is first used to parse the DIMD flag of the block, which is initialized to the default value 154. If flag == 0, the parsing continues normally. Otherwise (if the flag == 1), only the ISP index is parsed, and the following flags / indexes are presumed to be zero: BDPCM flag, MIP flag, MRL index. In this case, the entire IPM parsing is also skipped. During the parsing phase, when a regular non-DIMD block queries the IPM of its DIMD neighbor, the mode PLANAR_IDX is used as the virtual IPM of the DIMD block. 2.17.2 Texture analysis The texture analysis of DIMD includes gradient histogram (HoG) computation ( Figure 22)。The HoG calculation is performed by applying a horizontal Sobel filter and a vertical Sobel filter to the pixels in a template of width 3 around the block. Otherwise, if the above template pixels fall into different CTUs, they are not used for texture analysis. Once computed, the IPMs corresponding to the two highest histogram bins are selected for the block. In the previous version, all pixels in the middle row of the template were involved in the HoG calculation. However, the current version improves the throughput of the process by applying the Sobel filter more sparsely on 4×4 blocks. For this purpose, only one pixel from the left and one pixel from the upper side are used. This is shown in Figure 22 this. In addition to reducing the number of operations for gradient calculation, this property also simplifies the selection of the best 2 modes from HoG, since the resulting HoG cannot have more than two non-zero magnitudes. 2.17.3 Prediction Fusion The current method uses the fusion of three prediction values for each block. However, the selection of the prediction mode is different, and the proposed combined hypothesis intra prediction method is utilized, where the planar mode is considered for use in combination with other modes when computing intra prediction candidates. In the current version, the two IPMs corresponding to the two highest HoG bins are combined with the planar mode. The prediction fusion is applied as a weighted average of the above three prediction values. For this purpose, the weight of the plane is fixed to 21 / 64 (~1 / 3). Subsequently, the remaining 43 / 64 (approximately equal to 2 / 3) weight is shared proportionally with the two HoG IPMs according to the magnitude of their HoG bins. Figure 23 Visualize this process. 2.18. Template-based Intra-mode Derivation (TIMD) This proposal presents a template-based intra-mode derivation (TIMD) method using MPM, where TIMD modes are derived from MPM using neighboring templates. The TIMD modes are used as additional intra prediction methods for the CU. 2.18.1 TIMD Mode Derivation For each intra prediction mode in MPM, the SATD between its predicted samples and the reconstructed samples of the template is calculated. The intra prediction mode with the minimum SATD is selected as the TIMD mode and used for the intra prediction of the current CU. The position-dependent intra prediction combination (PDPC) is included in the derivation of the TIMD mode. 2.18.2. TIMD Signaling The flag is signaled in the Sequence Parameter Set (SPS) to enable / disable the proposed method. When the flag is true, the CU-level flag is signaled to indicate whether the proposed TIMD method is used. The TIMD flag is signaled to the right after the MIP flag. If the TIMD flag is equal to true, all the remaining syntax elements related to the intra prediction mode for luma (including MRL, ISP, and the regular parsing stage for the intra prediction mode for luma) are skipped. 2.18.3. Interaction with New Decoding Tools The DIMD method using planar prediction fusion has been integrated in EE2. When the EE2 DIMD flag is equal to true, the proposed TIMD flag is not signaled and is set to false. Similar to PDPC, gradient PDPC is also included in the derivation of the TIMD mode. When the secondary MPM is enabled, both the primary MPM and the secondary MPM are used to derive the TIMD mode. The 6-tap interpolation filter is not used to derive the TIMD mode. 2.18.4. Modifying the MPM List Construction in Deriving the TIMD Mode During the construction of the MPM list, the intra prediction mode of neighboring blocks is derived as planar when they are inter-coded. To improve the accuracy of the MPM list, when neighboring blocks are inter-coded, the motion vector and reference pictures are used to derive the propagated intra prediction mode, which is used in the construction of the MPM list. This modification is only applied to the derivation of the TIMD mode. 2.18.5 Using Fused TIMD Instead of only selecting one mode with the minimum SATD cost, this proposal proposes to select the top two modes with the minimum SATD cost for the intra prediction mode derived using the TIMD method, and perform weighted fusion on these two modes. The resulting weighted intra prediction is used to decode the current CU. The costs of the two selected modes are compared with a threshold. In the test, the cost factor 2 is applied as follows: costMode2 < 2 ′ costMode1 If this condition is true, fusion is applied; otherwise, only mode 1 is used. The weights of the modes are calculated from their SATD costs as follows: weight1 = costMode2 / (costMode1 + costMode2) weight2 = 1 – weight1 2.19. Convolutional Cross-Component Model (CCCM) for Intra Prediction A Convolutional Cross-Component Model (CCCM) is proposed to predict chroma samples from reconstructed luma samples in a similar spirit to that done by the current CCLM mode. Similar to CCLM, when chroma downsampling is used, the reconstructed luma samples are downsampled to match the lower resolution chroma grid. Similarly, similar to CCLM, there is an option to use a single model or multi-model variant of CCCM. The multi-model variant uses two models, one model derived for samples above an average luma reference value and the other model derived for the remaining samples (following the spirit of the CCLM design). The multi-model CCCM mode can be selected for a PU with at least 128 available reference samples. 2.19.1. Convolutional Filter The proposed convolutional 7-tap filter consists of a 5-tap plus shape spatial component, a non-linear term, and a bias term. The input to the 5-tap spatial component of the filter includes the central luma sample (C) at the same location as the chroma sample to be predicted, and its upper / north (N), lower / south (S), left / west (W), and right / east (E) neighbors, as illustrated below in Figure 24 . The non-linear term P is represented as a power of 2 in the central luma sample C and scaled to the sample value range of the content: P = (C * C + midVal) >> bitDepth That is, for 10-bit content, it is calculated as: P = (C * C + 512) >> 10 The bias term B represents a scalar offset between the input and the output (similar to the offset term in CCLM) and is set to the mid-chroma value (512 for 10-bit content). The output of the filter is calculated as the convolution between the filter coefficients c i and the input values, and is clipped to the range of valid chroma samples: predChromaVal = c0C + c1N + c2S + c3E + c4W + c5P + c6B 2.19.2. Calculation of Filter Coefficients The filter coefficients c are calculated by minimizing the Mean Squared Error (MSE) between the predicted chroma samples and the reconstructed chroma samples in the reference region. i . Figure 25A reference region composed of 6 rows of chroma samples on the upper and left sides of the PU is shown. The reference region extends one PU width to the right and one PU height below the PU boundary. The region is adjusted to include only available samples. The extended part shown in the blue area in the figure is required to support the "side sample" in the cross-shaped spatial filter, and filling is performed in the unavailable region. MSE minimization is performed by calculating the autocorrelation matrix for the luminance input and the cross-correlation vector between the luminance input and the chroma output. The autocorrelation matrix is LDL decomposed, and back substitution is used to calculate the final filter coefficients. This process generally follows the calculation of the ALF filter coefficients in ECM. However, LDL decomposition is chosen instead of Cholesky decomposition to avoid using square root operations. The proposed method uses only integer operations. 2.19.3 Bitstream Signaling The use of the mode is signaled by using CABAC to encode and decode the PU-level flag. A new CABAC context is included to support this process. When it comes to signaling, CCCM is considered a sub-mode of CCLM. That is, if the intra prediction mode is LM_CHROMA_IDX (to enable single-mode CCCM) or MMLM_CHROMA_IDX (to enable multi-model CCCM), then only the CCCM flag is signaled. 2.20. Gradient Linear Model (GLM) Compared with CCLM, instead of the downsampled luminance values, GLM uses the luminance sample gradient to derive the linear model. Specifically, when GLM is applied, the input to the CCLM process (i.e., the downsampled luminance sample L) is replaced by the luminance sample gradient G. The other parts of CCLM (e.g., parameter derivation, prediction sample linear transformation) remain unchanged. C = α·G + β For signaling, when the CCLM mode for the current CU is enabled, two flags are signaled for the Cb and Cr components respectively to indicate whether GLM is enabled for each component; if GLM is enabled for one component, then a syntax element is further signaled to select one of the 4 gradient filters for gradient calculation. · Four gradient filters are enabled for GLM, as Figure 26 shown. 2.21. Gradient and Location-Based Convolutional Cross-Component Model for Intra Prediction (GL-CCCM) The proposed GL-CCCM method uses gradient and location information instead of the 4 spatial neighboring samples in the CCCM filter. The GL-CCCM filter for prediction is: predChromaVal = c0C + c1Gy +c2G x +c3Y + c4X + c5P + c6B where G y and G x are the vertical gradient and the horizontal gradient respectively, and are calculated as: G y = (2N + NW + NE) - (2S + SW + SE) G x = (2W + NW + SW) - (2E + NE + SE) In addition, the Y and X parameters are the vertical position and the horizontal position of the central luminance sample, and they are calculated relative to the upper - left coordinates of the block. The remaining parameters are the same as those of the CCCM tool. The reference region for parameter calculation is the same as that of the CCCM method. Figure 27 The spatial samples for GL - CCCM are shown. Bit - stream signaling Use CABAC encoding / decoding of the PU - level flag to signal the use of the mode. A new CABAC context is included to support this process. When it comes to signaling, GL - CCCM is considered a sub - mode of CCCM. That is, the GL - CCCM flag is signaled only if the original CCCM flag is true. Encoder operation The encoder performs two new RD checks in the chrominance prediction mode loop, one for checking the single - model GL - CCCM mode and one for checking the multi - model GL - CCCM mode. 2.22. CCCM using non - subsampled luminance samples 2.22.1 Block level In this proposal, CCCM using non - subsampled luminance samples is proposed, where the chrominance samples are directly predicted from the original reconstructed luminance samples, i.e., without subsampling. As Figure 28 shown, the proposed CCCM filter consists of a 6 - tap spatial term, two non - linear terms, and a bias term. The 6 - tap spatial term corresponds to 6 neighboring luminance samples (i.e., L0, L1, …, L5) adjacent to the chrominance sample to be predicted (i.e., C). where α i is related to L iLet α be the coefficient associated with the offset and β be the offset. Similar to the existing CCCM design, up to 6 rows / columns of chroma samples on the upper side and left side of the current CU can be used to derive the filter coefficients. The filter coefficients are derived based on the same LDL decomposition method used in CCCM. In the proposal, in addition to the existing CCCM model, the proposed method is signaled as an additional CCCM model. For signaling, when CCCM is selected, a single flag is signaled and used for both chroma components to indicate whether the default CCCM model or the proposed CCCM model is applied. 2.22.2 High-level control The subsampling of the luma component may not be optimal for the derivation of the CCCM model for content with sharp details (such as SCC content). In this proposal, it is proposed to disable luma subsampling and directly derive and apply the model on the non-subsampled luma samples. If subsampling is not applied, the CCCM model shape is a 5×5 diamond. An SPS flag is signaled to indicate whether luma subsampling is applied for CCCM. 3. Problems 1. In the current design of CCCM based on gradient and position, horizontal gradient and vertical gradient are used. However, gradients in other directions (e.g., 45 degrees or 135 degrees) are not considered. In addition, in the CCCM model, only luma samples are used without considering neighboring chroma samples. Therefore, the coding and decoding performance of CCCM can be improved by considering gradients in other directions and neighboring chroma samples. 2. When non-downsampled luma samples are used for the CCCM model, a CCCM model shape of 3×2 or 5×5 diamond is used. However, for videos with different color formats and different resolutions, a constant shape may not be optimal. 3. In ECM-7.0, the GLM mode including the use of downsampled luma values is included. In addition, the CCCM mode is also designed to consider the downsampled luma values. Non-downsampled luma samples can be used for the GLM mode with luma values and / or CCCM models. In addition, the interactions with LM-T, LM-T, LM-TL, and multi-model LM should be considered. 4. Detailed solutions The following detailed solutions should be considered as examples to explain the general concepts. These solutions should not be interpreted in a narrow way. In addition, these solutions can be combined in any way. It should be noted that the CCCM method described below is not limited to the CCCM method introduced in the background art. Using gradients from different directions and chroma neighboring samples in CCCM 1. It is proposed that gradients calculated from one or more directions can be used in the CCCM model. a. One or more directions may include non-horizontal and non-vertical directions. b. In one example, whether to calculate the gradient and / or which one or more directions of the gradient are to be calculated depends on the luminance sample and / or the intra prediction direction and / or the mode of the luminance sample. c. In one example, how to calculate the gradient may depend on the direction associated with the gradient. d. In one example, the number of reference samples associated with the corresponding luminance block utilized in the CCCM / CCLM may depend on the direction. e. In one example, downsampled or non-downsampled luminance samples may be used to calculate the gradient. i. In one example, whether to calculate and / or how to calculate the gradient may depend on the color format. 1) In one example, the gradient may be calculated using downsampled luminance samples in 4:2:2 and 4:2:0 color formats. a) Alternatively, the gradient may be calculated using non-downsampled luminance samples in 4:2:2 color format. b) Alternatively, the gradient may be calculated using non-downsampled luminance samples in 4:2:0 color format. 2) In one example, the gradient may be calculated using non-downsampled luminance samples in 4:4:4. ii. In one example, whether to calculate and / or how to calculate the gradient may depend on the video content of the video unit. 1) In one example, the gradient may be calculated using non-downsampled luminance samples for screen content video. iii. In one example, the gradient calculated using downsampled luminance samples or non-downsampled luminance samples may be signaled or derived in the bitstream. f. In one example, gradients from one or more directions may be used. i. In one example, the gradient may be calculated using an M×M (e.g., M = 3) shape. 1) In one instance, a 45-degree gradient may be used. An example is shown in Figure 29A below. a) In one example, the gradient may be calculated as G = (a*NW + b*N + b*W) - (a*SE + b*S + b*E). i. In one example, a = 2 and b = 1. ii. In one example, a = 1 and b = 0. iii. In one example, a = 0 and b = 1. 2) In one example, a gradient of 135 degrees can be used, as shown in Figure 29B . a) In one example, the gradient can be calculated as G = (a * NE + b * N + b * E) - (a * SW + b * S + b * W). i. In one example, a = 2 and b = 1. ii. In one example, a = 1 and b = 0. iii. In one example, a = 0 and b = 1. 3) In one example, the gradient can be calculated as G = (a * NW + b * W) - (a * SE + b * E). The example is shown as Figure 29C . a) In one example, a = 1 and b = 1. 4) In one example, the gradient can be calculated as G = (a * NE + b * E) - (a * SW + b * W). The example is shown as Figure 29D . a) In one example, a = 1 and b = 1. 5) In one example, the gradient can be calculated as G = (a * NW + b * N) - (a * SE + b * S). The example is shown as Figure 29E . a) In one example, a = 1 and b = 1. 6) In one example, the gradient can be calculated as G = (a * NE + b * N) - (a * SW + b * S). The example is shown as Figure 29F . a) In one example, a = 1 and b = 1. ii. In one example, an M × N shape (e.g., M = 3, N = 2) can be used to calculate the gradient. 1) In one example, the gradient can be calculated as G = (a * NW + b * SW) - (a * NE + b * SE). The example is shown as Figure 30A . a) In one example, a = 1 and b = 1. 2) In one example, the gradient can be calculated as G = (a * NW + b * N + a * NE) - (a * SW + b * S + a * SE). The example is shown as Figure 30B . a) In one example, a = 1 and b = 2. b) In one example, a = 0 and b = 1. c) In one example, a = 1 and b = 0. 3) In one example, the gradient can be calculated as G = (a * NW + b * N + c * SW) - (c * NE + b * SE + b S). The examples are shown as Figure 30C . a) In one example, a = 2, b = 1, and c = 1. b) In one example, a = 1, b = 1, and c = 0. 4) In one example, the gradient can be calculated as G = (a * NE + b * N + c * SE) - (a * SW + b * S + c * NW). The examples are shown as Figure 30D . a) In one example, a = 2, b = 1, and c = 1. b) In one example, a = 1, b = 1, and c = 0. 2. It is proposed that at least one chromaticity neighboring sample can be used in the CCCM model. a. In one example, the chromaticity neighboring samples can be adjacent or non - adjacent. Denote the chromaticity neighboring samples as P( - n, y), P(x, - n), and P( - m, - n), such as n = 1 or n = 2, and m = - 1 or - 2, and x and y represent the horizontal position and vertical position of the central sample relative to the upper - left coordinates of the block. b. In one example, the chromaticity neighboring samples can be used with position information. i. In one example, (H - y) * P( - n, y) can be used, where H represents the block height. ii. In one example, (W - x) * P(x, - n) can be used, where W represents the block width. iii. In one example, (W - x + H - y) * P( - m, - n) can be used. c. In one instance, the coefficients on the chromaticity neighboring samples can be fixed values. d. In one instance, the coefficients on the chromaticity neighboring samples can depend on the positions of the chromaticity neighboring samples. e. In one instance, the coefficients on the chromaticity neighboring samples can be derived at the decoder. 3. In one example, the CCCM model using the above - mentioned gradient and / or chromaticity neighboring samples can be used as an additional CCCM mode or replace the existing CCCM mode. Adaptive shape for the CCCM model 4. It is proposed that one or more shapes can be used in the CCCM model. a. In one example, a diamond M1×M2 or a cross M2×N2 can be used, where M1 and M2 represent the number of columns of the sample points, and N1 and N2 represent the number of rows of the sample points. i. In one example, M1, N1, M2, and N2 can depend on the color format. 1) In one example, for 4:4:4 color format and 4:2:0 color format, M1 = N1. 2) In one example, for 4:4:4 color format and 4:2:0 color format, M2 = N2. 3) In one example, for 4:2:2 color format, M1 < N1. 4) In one example, for 4:2:2 color format, M2 < N2. ii. In one example, M1, N1, M2, and N2 can depend on the coding information. 1) In one example, the coding information can refer to the resolution of the video. a) In one example, M1 and / or N1 for low-resolution video can be less than M1 and / or N1 for high-resolution video. b) In one example, M2 and / or N2 for low-resolution video can be less than M2 and / or N2 for high-resolution video. 2) In one example, the codec information can refer to the syntax elements signaled at the SPS / PPS / PH / picture / SH / strip level. b. In one example, the determination of the shape used in the CCCM model can be signaled or derived in the bitstream. c. In one instance, whether to use a particular shape in the CCCM model can be signaled at the sequence level, group of pictures level, picture level, strip level, or slice group level. Chroma encoding and decoding using non-downsampled luma samples 5. The LM mode can be applied based on the non-downsampled luminance values. a. For example, for 4:2:0 (and / or 4:2:2, and / or 4:4:4) color format, the LM mode can be applied based on the non-downsampled luminance reconstruction sample points. b. For example, the LM model can be calculated based on the non-downsampled luminance reconstruction sample points adjacent to the current block. c. For example, the chrominance prediction sample points of the LM mode can be derived based on the non-downsampled luminance reconstruction sample points inside the current block. d. For example, LM can be one or more of the following: i. CCLM and / or its variants. ii. CCCM and / or its variants (e.g., GL-CCCM). iii. GLM and / or its variants (e.g., GLM with luminance values). iv. Additionally, the above LM can be LM-L that only considers the left neighbor. v. Additionally, the above LM can be LM-T that only considers the upper neighbor. vi. Additionally, the above LM can be LM-TL that considers both the left neighbor and the upper neighbor. vii. Additionally, the above LM can be single-model based. viii. Additionally, the above LM can be multi-model based. e. For example, GLM with a luminance value pattern can be applied based on the non-downsampled luminance samples. i. For example, it can be calculated based on predChromaVal = a0Y0 + a1Y1 + a2Y2 + a3Y3 + … + a n Yn + a n+1 G + a n+2 B, where G can refer to the type of gradient, B can refer to the offset (e.g., a constant such as midValue which is 512 for 10-bit content), and Y0, Y1, Y2, …, Yn can refer to the non-downsampled luminance reconstruction sample values. f. For example, the GL-CCCM mode can be applied based on the non-downsampled luminance samples. i. For example, it can be calculated based on predChromaVal = c0C + c1G y + c2G x + c3Y + c4X + c5P + c6B, where G y and G x are the vertical gradient and horizontal gradient calculated according to the non-downsampled luminance reconstruction sample values, and the Y and X parameters are the vertical position and horizontal position of the non-downsampled luminance samples relative to the upper left coordinate of the block. g. For example, variants of the LM mode can be applied based on the non-downsampled luminance samples. i. For example, it can be calculated based on predChromaVal = a0Y0 + a1Y1 + a2Y2 + a3Y3 + … + a n Yn + a n+1 B + a n+2 L, where B can refer to the offset, L can refer to a non-linear term or a linear term (e.g., the L term can be optional), and Y0, Y1, Y2, …, Yn can refer to the non-downsampled luminance reconstruction sample values. h. For example, it can be deduced whether to use non-downsampled luma samples for chroma encoding / decoding based on encoding / decoding information (such as histogram of gradients, histogram of colors, luma sample values). i. For example, it can be signaled whether to use non-downsampled luma samples based on SPS / PPS / PH / picture / SH / strip-level syntax elements. i. For example, a specific chroma mode can be signaled. ii. In addition, a specific chroma mode can refer to one or more LM modes (such as those listed in sub-item d above). j. For example, it can be signaled whether to use non-downloaded luma samples based on block-level syntax elements. i. For example, a certain chroma mode can be signaled. ii. In addition, a specific chroma mode can refer to one or more LM modes (such as those listed in sub-item d above). iii. For example, when not signaled, it can be presumed that non-downsampled luma samples are not used for the block. iv. In addition, whether to use non-downsampled luma samples can be conditional on whether LM-TL / LM-L / LM-T modes are used for the block. 1) Alternatively, whether to use LM-TL / LM-L / LM-T modes can be adjusted by whether non-downsampled luma samples are used for the block. v. In addition, whether to use non-downsampled luma samples can be conditional on whether multi-mode LM is used for the block. 1) Alternatively, whether to use single / multi-model LM modes can be adjusted by whether non-downsampled luma samples are used for the block. General requirements 6. Whether to apply and / or how to apply the methods disclosed above can be signaled at sequence level / group of pictures level / picture level / strip level / slice group level, such as in sequence header / picture header / SPS / VPS / DPS / DCI / PPS / PPS / strip header / slice group header. 7. Whether to apply and / or how to apply the methods disclosed above can be signaled at PB / TB / CB / PU / TU / CU / VPDU / CTU / CTU row / strip / slice / sub-picture / other types of regions containing more than one sample or pixel. 8. Whether to apply and / or how to apply the methods disclosed above can depend on encoding / decoding information, such as block size, color format, single / double-tree segmentation, color component, strip / picture type.

[0094] As used herein, the terms "video unit" or "video block" may be a sequence, picture, slice, tile, sub-picture, coding tree unit (CTU) / coding tree block (CTB), CTU / CTB row, one or more coding units (CU) / coding blocks (CB), one or more CTU / CTB, one or more virtual pipeline data units (VPDU), sub-regions within a picture / slice / tile. The terms "luma latent code" / "luma latent representation" as used herein may refer to a set of luma latent samples. The terms "chroma latent code" / "chroma latent representation" as used herein may refer to a set of chroma latent samples. The terms "latent sample" / "latent representation" as used herein may include luma latent samples and chroma latent samples.

[0095] Figure 31 FIG. 3100 shows a flowchart of a method 3100 for video processing according to an embodiment of the present invention. Method 3100 is implemented during the conversion between a target video block of a video and a bitstream of the video.

[0096] At block 3110, for the conversion between a video unit of a video and a bitstream of the video unit, gradients from one or more directions associated with the video unit are determined. A convolutional cross-component model (CCCM) model is applied to the video unit.

[0097] At block 3120, a prediction of the video unit is determined by using gradients from one or more directions.

[0098] At block 3130, a conversion is performed based on the prediction of the video unit. In some embodiments, the conversion may include an encoded video unit from the bitstream. Alternatively or additionally, the conversion may include a decoded video unit from the bitstream. In this way, the coding performance of the CCCM can be improved by considering gradients in other directions and neighboring chroma samples.

[0099] In some embodiments, the gradients are calculated using downsampled luma samples. Alternatively or additionally, the gradients are calculated using non-downsampled luma samples.

[0100] In some embodiments, whether to calculate and / or how to calculate the gradients depends on the video content of the video unit. In some embodiments, the gradients are calculated using non-downsampled luma samples for screen content video.

[0101] In some embodiments, whether the downsampled luma samples or the non-downsampled luma samples are used to calculate the gradients is signaled in the bitstream. Alternatively, whether the downsampled luma samples or the non-downsampled luma samples are used to calculate the gradients is derived.

[0102] In some embodiments, whether to calculate and / or how to calculate the gradient depends on the color format. In some embodiments, the gradient is calculated using downsampled luminance samples in 4:2:2 and 4:2:0 color formats. In some embodiments, the gradient is calculated using non-downsampled luminance samples in 4:2:2 color format. In some embodiments, the gradient is calculated using non-downsampled luminance samples in 4:2:0 color format. In some embodiments, the gradient is calculated using non-downsampled luminance samples in 4:4:4.

[0103] In some embodiments, the gradient is calculated using an M×M (e.g., M = 3) shape, and M is an integer. Alternatively, the gradient is calculated using an M×N shape (e.g., M = 3, N = 2). In this case, M and N can be integers respectively.

[0104] In some embodiments, a 45-degree gradient is used (e.g., as shown in Figure 29A . In some embodiments, the gradient is calculated as: G = (a*NW + b*N + b*W) - (a*SE + b*S + b*E), where G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NW represents the 45-degree direction between the upward and left directions, SE represents the 45-degree direction between the downward and right directions, and a and b are integers respectively. In some embodiments, a = 2 and b = 1. In some embodiments, a = 1 and b = 0. In some embodiments, a = 0 and b = 1.

[0105] In some embodiments, a 135-degree gradient is used (e.g., as shown in Figure 29B . In some embodiments, the gradient is calculated as: G = (a*NE + b*N + b*E) - (a*SW + b*S + b*W), where G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NE represents the 135-degree direction between the upward and right directions, SW represents the 135-degree direction between the downward and left directions, and a and b are integers respectively. In some embodiments, a = 2 and b = 1. In some embodiments, a = 1 and b = 0. In some embodiments, a = 0 and b = 1.

[0106] In some embodiments, the gradient is calculated as G = (a*NW + b*W) - (a*SE + b*E), e.g., as shown in Figure 29C . In this case, G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NW represents the direction between the upward and left directions, SE represents the direction between the downward and right directions, and a and b are integers respectively. In some embodiments, a = 1 and b = 1.

[0107] In some embodiments, the gradient is calculated as: G = (a * NE + b * E) - (a * SW + b * W). For example, as Figure 29D shown. In this case, where G represents the gradient, W represents the left direction, E represents the right direction, NE represents the direction between the upper direction and the right direction, SW represents the direction between the lower direction and the left direction, and a and b are integers respectively. In some embodiments, a = 1 and b = 1.

[0108] In some embodiments, the gradient is calculated as G = (a * NW + b * N) - (a * SE + b * S). For example, as Figure 29E shown. In this case, where G represents the gradient, N represents the upper direction, S represents the lower direction, E represents the right direction, NW represents the direction between the upper direction and the left direction, SE represents the direction between the lower direction and the right direction, and a and b are integers respectively. In some embodiments, a = 1 and b = 1.

[0109] In some embodiments, the gradient is calculated as G = (a * NE + b * N) - (a * SW + b * S). For example, as Figure 29F shown. In this case, where G represents the gradient, N represents the upper direction, S represents the lower direction, NE represents the direction between the upper direction and the right direction, SW represents the direction between the lower direction and the left direction, and a and b are integers respectively. In some embodiments, a = 1 and b = 1.

[0110] In some embodiments, the gradient is calculated as G = (a * NW + b * SW) - (a * NE + b * SE). For example, as Figure 30A shown. In this case, G represents the gradient, NW represents the direction between the upper direction and the left direction, SW represents the direction between the lower direction and the left direction, NE represents the direction between the upper direction and the right direction, and a and b are integers respectively. In some embodiments, a = 1 and b = 1.

[0111] In some embodiments, the gradient is calculated as: G = (a * NW + b * N + a * NE) - (a * SW + b * S + a * SE). For example, as Figure 30B shown. In this case, G represents the gradient, NW represents the direction between the upper direction and the left direction, N represents the upper direction, NE represents the direction between the upper direction and the right direction, SW represents the direction between the lower direction and the left direction, S represents the lower direction, SE represents the direction between the lower direction and the right direction, and a and b are integers respectively. In some embodiments, a = 1 and b = 2. Or, a = 0 and b = 1. Or, a = 1 and b = 0.

[0112] In some embodiments, the gradient is calculated as G = (a * NW + b * N + c * SW) - (c * NE + b * SE + b * S). For example, asFigure 30C As shown. In this case, G represents the gradient, NW represents the direction between the upward direction and the left direction, N represents the upward direction, SW represents the direction between the downward direction and the left direction, NE represents the direction between the upward direction and the right direction, S represents the downward direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively. In some embodiments, a = 2, b = 1, c = 1. Alternatively, a = 1, b = 1, c = 0.

[0113] In some embodiments, the gradient is calculated as G = (a * NE + b * N + c * SE) - (a * SW + b * S + c * NW), for example, as Figure 30D shown. In this case, G represents the gradient, NE represents the direction between the upward direction and the right direction, N represents the upward direction, SE represents the direction between the downward direction and the right direction, SW represents the direction between the downward direction and the left direction, S represents the downward direction, NW represents the direction between the upward direction and the left direction, and a and b are integers respectively. In some embodiments, a = 2, b = 1, c = 1. Alternatively, a = 1, b = 1, c = 0.

[0114] In some embodiments, one or more of the directions include non - horizontal or non - vertical directions. In some embodiments, whether to calculate the gradient and / or which one or more of the directions of the gradient are to be calculated depends on one or more of the following: the luminance sample, the intra - prediction direction, or the mode of the luminance sample.

[0115] In some embodiments, the way of calculating the gradient depends on the direction associated with the gradient. In some embodiments, the number of reference samples associated with the corresponding luminance block utilized in the CCCM / CCLM depends on the direction.

[0116] In some embodiments, at least one chrominance neighboring sample is used in the CCCM model.

[0117] In some embodiments, the chrominance neighboring samples are adjacent or non - adjacent, where the chrominance neighboring samples are represented as P( - n, y), P(x, - n), and P( - m, - n), and x and y represent the horizontal position and the vertical position of the central sample relative to the upper - left coordinates of the video unit respectively. In some embodiments, n = 1 or n = 2 and m = - 1 or m = - 2. In some embodiments, the chrominance neighboring samples are used together with the position information. In some embodiments, (H - y) * P( - n, y) is used, where H represents the block height, P( - n, y) represents the chrominance neighboring sample, and y represents the vertical position of the central sample relative to the upper - left coordinates of the video unit.

[0118] In some embodiments, (W - x)*P(x, -n) is used, where W represents the block width, P(x, -n) represents the chrominance neighboring sample, and x represents the horizontal position of the central sample relative to the upper left coordinate of the video unit. In some embodiments, (W – x + H - y)*P(-n, -n) is used, where W represents the block width, H represents the block height, P(-n, -n) represents the chrominance neighboring sample, and x and y represent the horizontal position and the vertical position of the central sample relative to the upper left coordinate of the video unit, respectively.

[0119] In some embodiments, the coefficient on the chrominance neighboring sample is a fixed value. Alternatively, the coefficient on the chrominance neighboring sample depends on the position of the chrominance neighboring sample. Alternatively, the coefficient on the chrominance neighboring sample is derived at the decoder.

[0120] In some embodiments, one or more shapes are used in the CCCM model. In some embodiments, the determination of one or more shapes used in the CCCM model is indicated in the bitstream. Alternatively, the determination of one or more shapes used in the CCCM model is derived.

[0121] In some embodiments, a diamond shape of M1×N1 is used in the CCCM model, and where M1 represents the number of columns of samples, and N1 represents the number of rows of samples. Alternatively or additionally, a cross shape of M2×N2 is used in the CCCM model, and where M2 represents the number of columns of samples, and N2 represents the number of rows of samples.

[0122] In some embodiments, M1, N1, M2, and N2 depend on the color format. In some embodiments, M1 = N1 for 4:4:4 and 4:2:0 color formats. In some embodiments, M2 = N2 for 4:4:4 and 4:2:0 color formats. In some embodiments, M1 < N1 for 4:2:2 color format. In some embodiments, M2 < N2 for 4:2:2 color format. In some embodiments, M1, N1, M2, and N2 depend on the codec information.

[0123] In some embodiments, the codec information includes the resolution of the video. In some embodiments, at least one of M1 or N1 for low-resolution video is less than at least one of M1 or N1 for high-resolution video. In some embodiments, at least one of M2 or N2 for low-resolution video is less than at least one of M2 or N2 for high-resolution video. In some embodiments, the codec information includes syntax elements, and the syntax elements are signaled at one of a sequence parameter set (SPS), a picture parameter set (PPS), a picture header, a picture, a sequence header, or a slice level.

[0124] In some embodiments, whether to use the shape in the CCCM model is indicated at one of the sequence level, group of pictures (GOP) level, picture level, slice level, or tile level. In some embodiments, a CCCM model using gradient or chroma neighboring samples is used as an additional CCCM mode or to replace an existing CCCM mode.

[0125] In some embodiments, an indication of whether to determine and / or how to determine gradients from one or more directions associated with a video unit is indicated at one of the following: sequence level, group of pictures (GOP) level, picture level, slice level, or tile level. In some embodiments, an indication of whether to determine and / or how to determine gradients from one or more directions associated with a video unit is indicated in one of the following: sequence header, picture header, sequence parameter set (SPS), video parameter set (VPS), dependent parameter set (DPS), decoding capability information (DCI), picture parameter set (PPS), adaptive parameter set (APS), slice header, or tile header.

[0126] In some embodiments, an indication of whether to determine and / or how to determine gradients from one or more directions associated with a video unit is indicated in one of the following: prediction block (PB), transform block (TB), codec block (CB), prediction unit (PU), transform unit (TU), codec unit (CU), virtual pipeline data unit (VPDU), coding tree unit (CTU), CTU row, slice, tile, sub-picture, or a region containing more than one sample or pixel.

[0127] In some embodiments, method 3100 further includes: based on the codec information of the video unit, whether to determine and / or how to determine gradients from one or more directions associated with the video unit, where the codec information includes at least one of the following: block size, color format, single and / or dual-tree partitioning, color component, slice type, or picture type.

[0128] According to further embodiments of the present disclosure, a non-transitory computer-readable recording medium is provided. The non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing. The method includes: determining gradients from one or more directions associated with a video unit of the video, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and generating a bitstream based on the prediction of the video unit.

[0129] According to still other embodiments of the present disclosure, a method for storing a bitstream of a video is provided. The method includes: determining gradients from one or more directions associated with a video unit of the video, wherein a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; generating a bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

[0130] Figure 32 A flowchart of a method 3200 for video processing according to an embodiment of the present invention is shown. Method 3200 is implemented during the conversion between a target video block of a video and a bitstream of the video.

[0131] At block 3210, for the conversion between a video unit of a video and the bitstream of the video unit, a linear model (LM) mode is applied to the video unit based on the non-downsampled luminance values.

[0132] At block 3220, a prediction of the video unit is determined based on the LM mode.

[0133] At block 3230, a conversion is performed based on the prediction of the video unit. In some embodiments, the conversion may include an encoded video unit from the bitstream. Alternatively or additionally, the conversion may include a decoded video unit from the bitstream. This can improve the encoding performance and encoding efficiency.

[0134] In some embodiments, for at least one of the following: 4:2:0, 4:2:2, or 4:4:4 color formats, the LM mode is applied based on the non-downsampled luminance reconstruction samples. In some embodiments, the LM model is calculated based on the non-downsampled luminance reconstruction samples adjacent to the video unit.

[0135] In some embodiments, a gradient linear model (GLM) with a luminance value mode is applied based on the non-downsampled luminance samples. In some embodiments, the predicted chroma samples of the GLM with a luminance value mode are based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *G + a n+2* B is calculated, where predChromaVal represents the predicted chroma sample, G represents the type of gradient, B represents the offset, Y0, Y1, Y2, Y3, …, Yn respectively represent the non-downsampled luminance reconstruction sample values, and a0, a1, a2, a3, …, a n respectively represent the coefficients.

[0136] In some embodiments, an indication of whether to use non-downsampled luma samples is signaled based on a block-level syntax element. In some embodiments, an indication of whether to use non-downsampled luma samples is signaled for a chroma mode. In some embodiments, the chroma mode includes one or more LM modes.

[0137] In some embodiments, when an indication of whether to use non-downsampled luma samples is not signaled, non-downsampled luma samples are presumed not to be used for a video unit.

[0138] In some embodiments, an indication of whether to use non-downsampled luma samples is signaled based on whether at least one of an LM-top-left (LM-TL), LM-left (LM-L), or LM-top (LM-T) mode is used for a video unit. In some embodiments, whether at least one of LM-TL, LM-L, or LM-T is used is based on whether non-downsampled luma samples are used for a video unit.

[0139] In some embodiments, an indication of whether to use non-downsampled luma samples is signaled based on whether multi-mode LM is used for a video unit. In some embodiments, whether to use a single-model LM mode or a multi-model LM mode is based on whether non-downsampled luma samples are used for a video unit. In some embodiments, chroma prediction samples of the LM mode are derived based on non-downsampled luma reconstruction samples within a current block.

[0140] In some embodiments, a gradient and location based cross-component model (GL-CCCM) is applied based on non-downsampled luma samples. In some embodiments, predicted chroma samples of the GL-CCCM are calculated based on predChromaVal = c0*C + c1*Gy + c2*Gx + c3*Y + c4*X + c5*P + c6*B, where predChroma-Val represents predicted chroma samples, Gy and Gx represent vertical and horizontal gradients calculated from non-downsampled luma reconstruction sample values respectively, Y and X parameters represent the vertical and horizontal positions of non-downsampled luma samples relative to the top-left coordinates of a video unit, and c0, c1, c2, c3, c4, c5, and c6 represent coefficients respectively.

[0141] In some embodiments, a variant of the LM mode is applied based on non-downsampled luma samples. In some embodiments, predicted chroma samples of the variant of the LM mode are based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *B + a n+2*L is computed, where predChromaVal represents a predicted chroma sample, B represents an offset, L represents a non-linear or linear term, Y0, Y1, Y2, …, Yn represent the non-downsampled luminance reconstruction sample values respectively, and a0, a1, a2, a3, …, a n represent coefficients respectively.

[0142] In some embodiments, whether to use non-downsampled luminance samples for chroma encoding / decoding is derived based on encoding / decoding information. In some embodiments, the encoding / decoding information includes at least one of the following: gradient histogram, color histogram, or luminance sample value.

[0143] In some embodiments, an indication of whether to use non-downsampled luminance samples is signaled based on one of the following: SPS-level syntax element, PPS-level syntax element, PH-level syntax element, picture-level syntax element, SH-level syntax element, or slice-level syntax element. In some embodiments, an indication of whether to use non-downsampled luminance samples is signaled for a chroma mode. In some embodiments, the chroma mode includes one or more LM modes.

[0144] In some embodiments, the LM mode includes at least one of the following: CCLM mode, a variant of the CCLM mode, CCCM mode, a variant of the CCCM mode, GL-CCCM mode, GLM mode, a variant of the GLM mode, GLM with luminance values, LM-L considering only the left neighbor, LM-T considering only the upper neighbor, LM-TL considering both the left and upper neighbors, LM based on a single model, or LM based on multiple models.

[0145] In some embodiments, an indication of whether and / or how to apply an LM model to a video unit based on non-downsampled luminance values is indicated at one of the following: sequence level, group of pictures level group, picture level, slice level, or slice group level.

[0146] In some embodiments, an indication of whether and / or how to apply an LM model to a video unit based on non-downsampled luminance values is indicated in one of the following: sequence header, picture header, sequence parameter set (SPS), video parameter set (VPS), dependent parameter set (DPS), decoding capability information (DCI), picture parameter set (PPS), adaptive parameter set (APS), slice header, or slice group header.

[0147] In some embodiments, an indication of whether and / or how to apply an LM model to a video unit based on non-downsampled luminance values is included in one of the following: a prediction block (PB), a transform block (TB), a coding block (CB), a prediction unit (PU), a transform unit (TU), a coding unit (CU), a virtual pipeline data unit (VPDU), a coding tree unit (CTU), a CTU row, a slice, a picture, a sub-picture, or a region that includes more than one sample or pixel.

[0148] In some embodiments, method 3200 further includes: determining whether and / or how to apply an LM model to a video unit based on the coding information of the video unit, where the coding information includes at least one of the following: block size, color format, single and / or dual tree segmentation, color component, slice type, or picture type.

[0149] According to further embodiments of the present disclosure, a non-transitory computer-readable recording medium is provided. The non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing. The method includes: applying a linear model (LM) mode to a video unit of the video based on non-downsampled luminance values; determining a prediction of the video unit based on the LM mode; and generating a bitstream based on the prediction of the video unit.

[0150] According to still further embodiments of the present disclosure, a method for storing a bitstream of a video is provided. The method includes: applying a linear model (LM) mode to a video unit of the video based on non-downsampled luminance values; determining a prediction of the video unit based on the LM mode; generating a bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

[0151] Embodiments of the present disclosure may be described according to the following clauses, and the features may be combined in any reasonable manner.

[0152] Clause 1. A method for video processing, including: determining gradients from one or more directions associated with a video unit for conversion between the video unit of a video and a bitstream of the video unit, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and performing the conversion based on the prediction of the video unit.

[0153] Clause 2. The method according to Clause 1, where the gradients are calculated using downsampled luminance samples, or where the gradients are calculated using non-downsampled luminance samples.

[0154] Clause 3. The method according to Clause 2, wherein whether and / or how to calculate the gradient depends on the video content of the video unit.

[0155] Clause 4. The method according to Clause 3, wherein the gradient is calculated using the non-downsampled luminance samples for screen content video.

[0156] Clause 5. The method according to Clause 2, wherein whether the downsampled luminance samples or the non-downsampled luminance samples are used to calculate the gradient is signaled in the bitstream, or whether the downsampled luminance samples or the non-downsampled luminance samples are used to calculate the gradient is derived.

[0157] Clause 6. The method according to Clause 2, wherein whether and / or how to calculate the gradient depends on the color format.

[0158] Clause 7. The method according to Clause 6, wherein the gradient is calculated using the downsampled luminance samples in 4:2:2 and 4:2:0 color formats.

[0159] Clause 8. The method according to Clause 6, wherein the gradient is calculated using the non-downsampled luminance samples in 4:2:2 color format.

[0160] Clause 9. The method according to Clause 6, wherein the gradient is calculated using the non-downsampled luminance samples in 4:2:0 color format.

[0161] Clause 10. The method according to Clause 6, wherein the gradient is calculated using the non-downsampled luminance samples in 4:4:4.

[0162] Clause 11. The method according to Clause 1, wherein the gradient is calculated using an M×M shape, where M is an integer, or wherein the gradient is calculated using an M×N shape, where M and N are integers respectively.

[0163] Clause 12. The method according to Clause 11, wherein a 45-degree gradient is used.

[0164] Clause 13. The method according to Clause 12, wherein the gradient is calculated as: G = (a*NW + b*N + b*W) - (a*SE + b*S + b*E), where G represents the gradient, N represents the up direction, W represents the left direction, S represents the down direction, E represents the right direction, NW represents the 45-degree direction between the up direction and the left direction, SE represents the 45-degree direction between the down direction and the right direction, and a and b are integers respectively.

[0165] Clause 14. The method according to Clause 13, wherein a = 2 and b = 1, or wherein a = 1 and b = 0, or wherein a = 0 and b = 1.

[0166] Clause 15. The method according to Clause 11, wherein a gradient of 135 degrees is used.

[0167] Clause 16. The method according to Clause 15, wherein the gradient is calculated as: G = (a*NE + b*N + b*E) - (a*SW + b*S + b*W), where G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NE represents the 135-degree direction between the upward direction and the right direction, SW represents the 135-degree direction between the downward direction and the left direction, and a and b are integers respectively.

[0168] Clause 17. The method according to Clause 16, wherein a = 2 and b = 1, or wherein a = 1 and b = 0, or wherein a = 0 and b = 1.

[0169] Clause 18. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NW + b*W) - (a*SE + b*E), and wherein G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NW represents the direction between the upward direction and the left direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

[0170] Clause 19. The method according to Clause 18, wherein a = 1 and b = 1.

[0171] Clause 20. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NE + b*E) - (a*SW + b*W), where G represents the gradient, W represents the left direction, E represents the right direction, NE represents the direction between the upward direction and the right direction, SW represents the direction between the downward direction and the left direction, and a and b are integers respectively.

[0172] Clause 21. The method according to Clause 20, wherein a = 1 and b = 1.

[0173] Clause 22. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NW + b*N) - (a*SE + b*S), where G represents the gradient, N represents the upward direction, S represents the downward direction, E represents the right direction, NW represents the direction between the upward direction and the left direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

[0174] Clause 23. The method according to Clause 22, wherein a = 1 and b = 1.

[0175] Clause 24. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NE + b*N) - (a*SW + b*S), where G represents the gradient, N represents the upward direction, S represents the downward direction, NE represents the direction between the upward direction and the right direction, SW represents the direction between the downward direction and the left direction, and a and b are integers respectively.

[0176] Clause 25. The method according to Clause 24, wherein a = 1 and b = 1.

[0177] Clause 26. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NW + b*SW) - (a*NE + b*SE), where G represents the gradient, NW represents the direction between the upward direction and the left direction, SW represents the direction between the downward direction and the left direction, NE represents the direction between the upward direction and the right direction, and a and b are integers respectively.

[0178] Clause 27. The method according to Clause 26, wherein a = 1 and b = 1.

[0179] Clause 28. The method according to Clause 11, wherein the gradient is calculated as: G = (a*NW + b*N + a*NE) - (a*SW + b*S + a*SE), where G represents the gradient, NW represents the direction between the upward direction and the left direction, N represents the upward direction, NE represents the direction between the upward direction and the right direction, SW represents the direction between the downward direction and the left direction, S represents the downward direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

[0180] Clause 29. The method according to Clause 28, wherein a = 1 and b = 2, or wherein a = 0 and b = 1, or wherein a = 1 and b = 0.

[0181] Clause 30. The method according to Clause 11, wherein the gradient is calculated as G = (a*NW + b*N + c*SW) - (c*NE + b*SE + b*S), where G represents the gradient, NW represents the direction between the upward direction and the left direction, N represents the upward direction, SW represents the direction between the downward direction and the left direction, NE represents the direction between the upward direction and the right direction, S represents the downward direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

[0182] Clause 31. The method according to Clause 30, wherein a = 2, b = 1, and c = 1, or wherein a = 1, b = 1, and c = 0.

[0183] Clause 32. The method according to Clause 11, wherein the gradient is calculated as G = (a*NE + b*N + c*SE) - (a*SW + b*S + c*NW), where G represents the gradient, NE represents the direction between the upper direction and the right direction, N represents the upper direction, SE represents the direction between the lower direction and the right direction, SW represents the direction between the lower direction and the left direction, S represents the lower direction, NW represents the direction between the upper direction and the left direction, and a and b are integers respectively.

[0184] Clause 33. The method according to Clause 32, wherein a = 2, b = 1, and c = 1, or wherein a = 1, b = 1, or c = 0.

[0185] Clause 34. The method according to Clause 1, wherein the one or more directions include non-horizontal directions or non-vertical directions.

[0186] Clause 35. The method according to Clause 1, wherein whether to calculate the gradient and / or which one or more directions of the gradient to be calculated depends on one or more of the following: a luminance sample point, an intra prediction direction, or a mode of the luminance sample point.

[0187] Clause 36. The method according to Clause 1, wherein the manner of calculating the gradient depends on the direction associated with the gradient.

[0188] Clause 37. The method according to Clause 1, wherein the number of reference sample points associated with the corresponding luminance block utilized in the CCCM / CCLM depends on the direction.

[0189] Clause 38. The method according to Clause 1, wherein at least one chrominance neighboring sample point is used in the CCCM model.

[0190] Clause 39. The method according to Clause 38, wherein the chrominance neighboring sample points are adjacent or non-adjacent. The chrominance neighboring sample points are represented as P(-n, y), P(x, -n), and P(-m, -n), and x and y respectively represent the horizontal position and the vertical position of the central sample point relative to the upper left coordinates of the video unit.

[0191] Clause 40. The method according to Clause 39, wherein n = 1 or n = 2 and m = -1 or m = -2.

[0192] Clause 41. The method according to Clause 38, wherein the chrominance neighboring sample points are used together with position information.

[0193] Clause 42. The method according to Clause 41, wherein (H - y)*P(-n, y) is used, where H represents the block height, P(-n, y) represents a chrominance neighboring sample, and y represents the vertical position of the central sample relative to the upper left coordinates of the video unit, or wherein (W - x)*P(x, -n) is used, where W represents the block width, P(x, -n) represents a chrominance neighboring sample, and x represents the horizontal position of the central sample relative to the upper left coordinates of the video unit.

[0194] Clause 43. The method according to Clause 41, wherein (W – x + H - y)*P(-n, -n) is used, where W represents the block width, H represents the block height, P(-n, -n) represents a chrominance neighboring sample, and x and y respectively represent the horizontal position and the vertical position of the central sample relative to the upper left coordinates of the video unit.

[0195] Clause 44. The method according to any one of Clauses 38 to 43, wherein the coefficient on the chrominance neighboring sample is a fixed value, or wherein the coefficient on the chrominance neighboring sample depends on the position of the chrominance neighboring sample, or wherein the coefficient on the chrominance neighboring sample is derived at the decoder.

[0196] Clause 45. The method according to Clause 1, wherein one or more shapes are used in the CCCM model.

[0197] Clause 46. The method according to Clause 45, wherein the determination of the one or more shapes used in the CCCM model is indicated in the bitstream, or wherein the determination of the one or more shapes used in the CCCM model is derived.

[0198] Clause 47. The method according to Clause 45, wherein a diamond shape with M1×N1 is used in the CCCM model, and wherein M1 represents the number of columns of samples, and N1 represents the number of rows of samples; and / or wherein a cross shape with M2×N2 is used in the CCCM model, and wherein M2 represents the number of columns of samples, and N2 represents the number of rows of samples.

[0199] Clause 48. The method according to Clause 47, wherein M1, N1, M2, and N2 depend on the color format.

[0200] Clause 49. The method according to Clause 48, wherein M1 = N1 for 4:4:4 and 4:2:0 color formats.

[0201] Clause 50. The method according to Clause 48, wherein M2 = N2 for 4:4:4 and 4:2:0 color formats.

[0202] Clause 51. The method according to Clause 48, wherein for a 4:2:2 color format, M1 < N1.

[0203] Clause 52. The method according to Clause 48, wherein for a 4:2:2 color format, M2 < N2.

[0204] Clause 53. The method according to Clause 47, wherein M1, N1, M2, and N2 depend on codec information.

[0205] Clause 54. The method according to Clause 53, wherein the codec information includes the resolution of the video.

[0206] Clause 55. The method according to Clause 54, wherein at least one of M1 or N1 for a low-resolution video is less than at least one of M1 or N1 for a high-resolution video.

[0207] Clause 56. The method according to Clause 54, wherein at least one of M2 or N2 for a low-resolution video is less than at least one of M2 or N2 for a high-resolution video.

[0208] Clause 57. The method according to Clause 53, wherein the codec information includes syntax elements that are signaled at one of a sequence parameter set (SPS), a picture parameter set (PPS), a picture header, a picture, a sequence header, or a slice level.

[0209] Clause 58. The method according to Clause 45, wherein whether to use the shape in the CCCM model is indicated at one of a sequence level, a group of pictures level, a picture level, a slice level, or a slice group level.

[0210] Clause 59. The method according to any one of Clauses 1 to 58, wherein the CCCM model using the gradient or the chrominance neighboring samples is used as an additional CCCM mode or to replace an existing CCCM mode.

[0211] Clause 60. The method according to any one of Clauses 1 to 59, wherein the indication of whether to determine and / or how to determine the gradient from the one or more directions associated with the video unit is indicated at one of the following: sequence level, group of pictures level, picture level, slice level, or slice group level.

[0212] Clause 61. The method according to any one of Clauses 1 to 59, wherein whether to determine and / or how to determine an indication of the gradient from the one or more directions associated with the video unit is indicated in one of the following: sequence header, picture header, sequence parameter set (SPS), video parameter set (VPS), dependent parameter set (DPS), decoding capability information (DCI), picture parameter set (PPS), adaptive parameter set (APS), slice header, or slice group header.

[0213] Clause 62. The method according to any one of Clauses 1 to 59, wherein whether to determine and / or how to determine an indication of the gradient from the one or more directions associated with the video unit is indicated in one of the following: prediction block (PB), transform block (TB), codec block (CB), prediction unit (PU), transform unit (TU), codec unit (CU), virtual pipeline data unit (VPDU), codec tree unit (CTU), CTU row, slice, picture, sub-picture, or a region containing more than one sample or pixel.

[0214] Clause 63. The method according to any one of Clauses 1 to 59, further comprising: based on the codec information of the video unit, whether to determine and / or how to determine the gradient from the one or more directions associated with the video unit, the codec information including at least one of the following: block size, color format, single and / or double tree segmentation, color component, slice type, or picture type.

[0215] Clause 64. A method for video processing, comprising: for the conversion between a video unit of a video and a bitstream of the video unit, applying a linear model (LM) mode to the video unit based on non-downsampled luminance values; determining a prediction of the video unit based on the LM mode; and performing the conversion based on the prediction of the video unit.

[0216] Clause 65. The method according to Clause 64, wherein the LM mode is applied based on non-downsampled luminance reconstruction samples for at least one of the following: 4:2:0, 4:2:2, or 4:4:4 color formats.

[0217] Clause 66. The method according to Clause 64, wherein the LM model is calculated based on non-downsampled luminance reconstruction samples adjacent to the video unit.

[0218] Clause 67. The method according to Clause 64, wherein a gradient linear model (GLM) with a luminance value mode is applied based on non-downsampled luminance samples.

[0219] Clause 68. The method according to Clause 67, wherein the predicted chroma sample points of the GLM with a brightness value pattern are based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *G + a n+2 *B is calculated, where predChromaVal represents the predicted chroma sample points, G represents the type of gradient, B represents the offset, Y0, Y1, Y2, Y3, …, Yn respectively represent the non-downsampled luminance reconstruction sample point values, and a0, a1, a2, a3, …, a n respectively represent coefficients.

[0220] Clause 69. The method according to Clause 64, wherein an indication of whether to use non-downsampled luminance samples is signaled based on a block-level syntax element.

[0221] Clause 70. The method according to Clause 69, wherein the indication of whether to use non-downsampled luminance samples is signaled for a chroma mode.

[0222] Clause 71. The method according to Clause 70, wherein the chroma mode includes one or more LM modes.

[0223] Clause 72. The method according to Clause 69, wherein when the indication of whether to use non-downsampled luminance samples is not signaled, the non-downsampled luminance samples are presumed not to be used for the video unit.

[0224] Clause 73. The method according to Clause 69, wherein the indication of whether to use non-downsampled luminance samples is signaled based on whether at least one of the LM-Top-Left (LM-TL), LM-Left (LM-L), or LM-Top (LM-T) modes is used for the video unit.

[0225] Clause 74. The method according to Clause 69, wherein whether at least one of LM-TL, LM-L, or LM-T is used is based on whether non-downsampled luminance samples are used for the video unit.

[0226] Clause 75. The method according to Clause 69, wherein the indication of whether to use non-downsampled luminance samples is signaled based on whether multi-mode LM is used for the video unit.

[0227] Clause 76. The method according to Clause 69, wherein whether to use a single-model LM mode or a multi-model LM mode is based on whether non-downsampled luminance samples are used for the video unit.

[0228] Clause 77. The method according to Clause 64, wherein the chrominance prediction samples of the LM mode are derived based on the non-downsampled luminance reconstruction samples within the current block.

[0229] Clause 78. The method according to Clause 64, wherein a gradient and location based convolutional cross-component model (GL-CCCM) is applied based on the non-downsampled luminance samples.

[0230] Clause 79. The method according to Clause 78, wherein the predicted chrominance samples of the GL-CCCM are calculated based on predChromaVal = c0*C + c1*Gy + c2*Gx + c3*Y + c4*X + c5*P + c6*B, where predChroma-Val represents the predicted chrominance samples, Gy and Gx respectively represent the vertical gradient and horizontal gradient calculated from the non-downsampled luminance reconstruction sample values, Y and X parameters represent the vertical position and horizontal position of the non-downsampled luminance samples relative to the upper left coordinates of the video unit, and c0, c1, c2, c3, c4, c5 and c6 respectively represent coefficients.

[0231] Clause 80. The method according to Clause 64, wherein a variant of the LM mode is applied based on the non-downsampled luminance samples.

[0232] Clause 81. The method according to Clause 80, wherein the predicted chrominance samples of the variant of the LM mode are based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *B + a n+2 *L is calculated, where predChromaVal represents the predicted chrominance samples, B represents an offset, L represents a non-linear or linear term, Y0, Y1, Y2, …, Yn respectively represent the non-downsampled luminance reconstruction sample values, and a0, a1, a2, a3, …, a n respectively represent coefficients.

[0233] Clause 82. The method according to Clause 64, wherein whether to use non-downsampled luminance samples for chrominance encoding and decoding is derived based on the encoding and decoding information.

[0234] Clause 83. The method according to Clause 82, wherein the encoding and decoding information includes at least one of the following: gradient histogram, color histogram or luminance sample values.

[0235] Clause 84. The method according to Clause 64, wherein an indication of whether to use non-downsampled luma samples is signaled based on one of the following: SPS-level syntax elements, PPS-level syntax elements, PH-level syntax elements, picture-level syntax elements, SH-level syntax elements, or slice-level syntax elements.

[0236] Clause 85. The method according to Clause 84, wherein the indication of whether to use non-downsampled luma samples is signaled for a chroma mode.

[0237] Clause 86. The method according to Clause 85, wherein the chroma mode includes one or more LM modes.

[0238] Clause 87. The method according to any one of Clauses 64 to 86, wherein the LM mode includes at least one of the following: CCLM mode, a variant of the CCLM mode, CCCM mode, a variant of the CCCM mode, GL-CCCM mode, GLM mode, a variant of the GLM mode, GLM with luma values, LM-L considering only the left neighbor, LM-T considering only the upper neighbor, LM-TL considering both the left and upper neighbors, single-model-based LM, or multi-model-based LM.

[0239] Clause 88. The method according to any one of Clauses 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on non-downsampled luma values is indicated at one of the following: sequence level, group of pictures level, picture level, slice level, or slice group level.

[0240] Clause 89. The method according to any one of Clauses 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on non-downsampled luma values is indicated in one of the following: sequence header, picture header, sequence parameter set (SPS), video parameter set (VPS), dependent parameter set (DPS), decoding capability information (DCI), picture parameter set (PPS), adaptive parameter set (APS), slice header, or slice group header.

[0241] Clause 90. The method according to any one of Clauses 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on non-downsampled luma values is included in one of the following: prediction block (PB), transform block (TB), codec block (CB), prediction unit (PU), transform unit (TU), codec unit CU, virtual pipeline data unit (VPDU), codec tree unit (CTU), CTU row, slice, tile, sub-picture, or a region containing more than one sample or pixel.

[0242] Clause 91. The method according to any one of Clauses 64 to 86 further includes: determining whether and / or how to apply the LM model to the video unit based on the downsampled luminance value, where the encoded and decoded information includes at least one of the following: block size, color format, single and / or double tree segmentation, color component, stripe type, or picture type.

[0243] Clause 92. The method according to any one of Clauses 1 to 91, where the conversion includes encoding the video unit into the bitstream.

[0244] Clause 93. The method according to any one of Clauses 1 to 91, where the conversion includes decoding the video unit from the bitstream.

[0245] Clause 94. An apparatus for video processing includes a processor and a non-transitory memory having instructions, where the instructions, when executed by the processor, cause the processor to perform the method according to any one of Clauses 1 to 93.

[0246] Clause 95. A non-transitory computer-readable storage medium stores instructions that cause a processor to perform the method according to any one of Clauses 1 to 93.

[0247] Clause 96. A non-transitory computer-readable recording medium stores a bitstream of a video, where the bitstream of the video is generated by a method performed by an apparatus for video processing, and the method includes: determining gradients from one or more directions associated with a video unit of the video, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and generating the bitstream based on the prediction of the video unit.

[0248] Clause 97. A method for storing a bitstream of a video includes: determining gradients from one or more directions associated with a video unit of the video, where a convolutional cross-component model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; generating the bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

[0249] Clause 98. A non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing. The method includes: applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; determining a prediction of the video unit based on the LM mode; and generating the bitstream based on the prediction of the video unit.

[0250] Clause 99. A method for storing a bitstream of a video includes: applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; determining a prediction of the video unit based on the LM mode; generating the bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium. Example device

[0251] Figure 33 A block diagram of a computing device 3300 in which various embodiments of the present disclosure may be implemented is shown. The computing device 3300 may be implemented as the source device 110 (or the video encoder 114 or 200) or the destination device 120 (or the video decoder 124 or 300), or may be included in the source device 110 (or the video encoder 114 or 200) or the destination device 120 (or the video decoder 124 or 300).

[0252] It should be understood that Figure 33 the computing device 3300 shown is for illustrative purposes only and does not imply any limitation to the functionality and scope of the embodiments of the present disclosure in any way.

[0253] As Figure 33 shown, the computing device 3300 includes a general-purpose computing device 3300. The computing device 3300 may include at least one or more processors or processing units 3310, a memory 3320, a storage unit 3330, one or more communication units 3340, one or more input devices 3350, and one or more output devices 3360.

[0254] In some embodiments, computing device 3300 may be implemented as any user terminal or server terminal having computing capabilities. The server terminal may be a server provided by a service provider, a large computing device, etc. The user terminal may be, for example, any type of mobile terminal, fixed terminal or portable terminal, including mobile phones, stations, units, devices, multimedia computers, multimedia tablet computers, Internet nodes, communicators, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / video cameras, positioning devices, television receivers, radio broadcast receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. It is contemplated that computing device 3300 may support any type of interface to the user (such as "wearable" circuitry, etc.).

[0255] Processing unit 3310 may be a physical processor or a virtual processor, and may implement various processes based on programs stored in memory 3320. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of computing device 3300. Processing unit 3310 may also be referred to as a central processing unit (CPU), microprocessor, controller or microcontroller.

[0256] Computing device 3300 generally includes various computer storage media. Such media may be any media accessible by computing device 3300, including but not limited to volatile media and non-volatile media, or removable media and non-removable media. Memory 3320 may be volatile memory (e.g., registers, caches, random access memory (RAM)), non-volatile memory (such as read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM) or flash memory), or any combination thereof. Storage unit 3330 may be any removable or non-removable media, and may include machine-readable media, such as memory, flash drive, disk, or other media that can be used to store information and / or data and can be accessed in computing device 3300.

[0257] Computing device 3300 may also include additional removable / non-removable storage media, volatile / non-volatile storage media. Although not shown in Figure 33 a disk drive for reading from and / or writing to a removable non-volatile disk, and an optical disk drive for reading from and / or writing to a removable non-volatile optical disk may be provided. In this case, each drive may be connected to a bus (not shown) via one or more data media interfaces.

[0258] The communication unit 3340 communicates with another computing device via a communication medium. Additionally, the functionality of the components in the computing device 3300 can be implemented by a single computing cluster or multiple computer machines, which can communicate via a communication connection. Thus, the computing device 3300 can operate in a networked environment using a logical connection with one or more other servers, networked personal computers (PCs), or other general network nodes.

[0259] The input device 3350 can be one or more of a variety of input devices, such as a mouse, keyboard, trackball, voice input device, and so on. The output device 3360 can be one or more of a variety of output devices, such as a display, speaker, printer, and so on. With the aid of the communication unit 3340, the computing device 3300 can also communicate with one or more external devices (not shown), such as storage devices and display devices, the computing device 3300 can also communicate with one or more devices that enable a user to interact with the computing device 3300, or if needed, the computing device 3300 can also communicate with any device (such as a network card, modem, etc.) that enables the computing device 3300 to communicate with one or more other computing devices. Such communication can be carried out via an input / output (I / O) interface (not shown).

[0260] In some embodiments, some or all of the components of the computing device 3300 can also be arranged in a cloud computing architecture rather than being integrated in a single device. In a cloud computing architecture, the components can be provided remotely and work together to implement the functions described in the present disclosure. In some embodiments, cloud computing provides computing, software, data access, and storage services, which do not require the end user to be aware of the physical location or configuration of the system or hardware providing these services. In various embodiments, cloud computing uses suitable protocols to provide services via a wide area network (such as the Internet). For example, a cloud computing provider provides an application via a wide area network, and the application can be accessed via a web browser or any other computing component. The software or components of the cloud computing architecture and the corresponding data can be stored on a server at a remote location. The computing resources in a cloud computing environment can be consolidated or distributed at the locations of remote data centers. The cloud computing infrastructure can provide services through a shared data center, although to the user, they appear as a single access point. Thus, a cloud computing architecture can be used to provide the components and functions described herein from a service provider at a remote location. Alternatively, the components and functions described herein can be provided by a conventional server or directly or otherwise installed on a client device.

[0261] In an embodiment of the present disclosure, the computing device 3300 can be used to implement video encoding / decoding. The memory 3320 can include one or more video codec modules 3325 having one or more program instructions. These modules are accessible and executable by the processing unit 3310 to perform the functions of the various embodiments described herein.

[0262] In an example embodiment of performing video encoding, the input device 3350 can receive video data as the input 3370 to be encoded. The video data can be processed, for example, by the video codec module 3325 to generate an encoded bitstream. The encoded bitstream can be provided as the output 3380 via the output device 3360.

[0263] In an example embodiment of performing video decoding, the input device 3350 can receive the encoded bitstream as the input 3370. The encoded bitstream can be processed, for example, by the video codec module 3325 to generate decoded video data. The decoded video data can be provided as the output 3380 via the output device 3360.

[0264] Although the present disclosure has been specifically shown and described with reference to preferred embodiments of the present disclosure, those skilled in the art will understand that various changes in form and detail may be made without departing from the spirit and scope of the present application as defined by the appended claims. These variations are intended to be covered by the scope of the present application. Therefore, the foregoing description of the embodiments of the present application is not intended to be limiting.

Claims

1. A method for video processing, comprising: Determining gradients from one or more directions associated with a video unit for conversion between the video unit of a video and the bitstream of the video unit, wherein a convolutional cross-component model (CCCM) model is applied to the video unit; Determining a prediction of the video unit by using the gradients from the one or more directions; And Performing the conversion based on the prediction of the video unit.

2. The method according to claim 1, wherein the gradients are calculated using downsampled luminance samples, or wherein the gradients are calculated using non-downsampled luminance samples.

3. The method according to claim 2, wherein whether to calculate and / or how to calculate the gradients depends on the video content of the video unit.

4. The method according to claim 3, wherein the gradients are calculated using non-downsampled luminance samples for screen content video.

5. The method according to claim 2, wherein the downsampled luminance samples or the non-downsampled luminance samples used to calculate the gradients are signaled in the bitstream, or the downsampled luminance samples or the non-downsampled luminance samples used to calculate the gradients are derived.

6. The method according to claim 2, wherein whether to calculate and / or how to calculate the gradients depends on the color format.

7. The method according to claim 6, wherein the gradients are calculated using the downsampled luminance samples in 4:2:2 and 4:2:0 color formats.

8. The method according to claim 6, wherein the gradients are calculated using the non-downsampled luminance samples in 4:2:2 color format.

9. The method according to claim 6, wherein the gradients are calculated using the non-downsampled luminance samples in 4:2:0 color format.

10. The method according to claim 6, wherein the gradients are calculated using the non-downsampled luminance samples in 4:4:

4.

11. The method according to claim 1, wherein the gradients are calculated using an M×M shape, and M is an integer, or wherein the gradients are calculated using an M×N shape, where M and N are integers respectively.

12. The method according to claim 11, wherein a 45-degree gradient is used.

13. The method according to claim 12, wherein the gradient is calculated as: G = (a*NW + b*N + b*W) - (a*SE + b*S + b*E), where G represents the gradient, N represents the up direction, W represents the left direction, S represents the down direction, E represents the right direction, NW represents the 45-degree direction between the up direction and the left direction, SE represents the 45-degree direction between the down direction and the right direction, and a and b are integers respectively.

14. The method according to claim 13, wherein a = 2 and b = 1, or wherein a = 1 and b = 0, or wherein a = 0 and b = 1.

15. The method according to claim 11, wherein a 135-degree gradient is used.

16. The method according to claim 15, wherein the gradient is calculated as: G = (a * NE + b * N + b * E) - (a * SW + b * S + b * W), where G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NE represents the 135-degree direction between the upward direction and the right direction, SW represents the 135-degree direction between the downward direction and the left direction, and a and b are integers respectively.

17. The method according to claim 16, wherein a = 2 and b = 1, or wherein a = 1 and b = 0, or wherein a = 0 and b = 1.

18. The method according to claim 11, wherein the gradient is calculated as: G = (a * NW + b * W) - (a * SE + b * E), and where G represents the gradient, N represents the upward direction, W represents the left direction, S represents the downward direction, E represents the right direction, NW represents the direction between the upward direction and the left direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

19. The method according to claim 18, wherein a = 1 and b = 1.

20. The method according to claim 11, wherein the gradient is calculated as: G = (a * NE + b * E) - (a * SW + b * W), where G represents the gradient, W represents the left direction, E represents the right direction, NE represents the direction between the upward direction and the right direction, SW represents the direction between the downward direction and the left direction, and a and b are integers respectively.

21. The method according to claim 20, wherein a = 1 and b = 1.

22. The method according to claim 11, wherein the gradient is calculated as: G = (a * NW + b * N) - (a * SE + b * S), where G represents the gradient, N represents the upward direction, S represents the downward direction, E represents the right direction, NW represents the direction between the upward direction and the left direction, SE represents the direction between the downward direction and the right direction, and a and b are integers respectively.

23. The method according to claim 22, wherein a = 1 and b = 1.

24. The method according to claim 11, wherein the gradient is calculated as: G = (a * NE + b * N) - (a * SW + b * S), where G represents the gradient, N represents the upward direction, S represents the downward direction, NE represents the direction between the upward direction and the right direction, SW represents the direction between the downward direction and the left direction, and a and b are integers respectively.

25. The method according to claim 24, wherein a = 1 and b = 1.

26. The method according to claim 11, wherein the gradient is calculated as: G = (a * NW + b * SW) - (a * NE + b * SE), where G represents the gradient, NW represents the direction between the upward direction and the left direction, SW represents the direction between the downward direction and the left direction, NE represents the direction between the upward direction and the right direction, and a and b are integers respectively.

27. The method according to claim 26, wherein a = 1 and b = 1.

28. The method according to claim 11, wherein the gradient is calculated as: G = (a*NW + b*N + a*NE) - (a*SW + b*S + a*SE), where G represents the gradient, NW represents the direction between the upper direction and the left direction, N represents the upper direction, NE represents the direction between the upper direction and the right direction, SW represents the direction between the lower direction and the left direction, S represents the lower direction, SE represents the direction between the lower direction and the right direction, and a and b are integers respectively.

29. The method according to claim 28, wherein a = 1 and b = 2, or where a = 0 and b = 1, or where a = 1 and b = 0.

30. The method according to claim 11, wherein the gradient is calculated as G = (a*NW + b*N + c*SW) - (c*NE + b*SE + b*S), where G represents the gradient, NW represents the direction between the upper direction and the left direction, N represents the upper direction, SW represents the direction between the lower direction and the left direction, NE represents the direction between the upper direction and the right direction, S represents the lower direction, SE represents the direction between the lower direction and the right direction, and a and b are integers respectively.

31. The method according to claim 30, wherein a = 2, b = 1, and c = 1, or where a = 1, b = 1, and c = 0.

32. The method according to claim 11, wherein the gradient is calculated as G = (a*NE + b*N + c*SE) - (a*SW + b*S + c*NW), where G represents the gradient, NE represents the direction between the upper direction and the right direction, N represents the upper direction, SE represents the direction between the lower direction and the right direction, SW represents the direction between the lower direction and the left direction, S represents the lower direction, NW represents the direction between the upper direction and the left direction, and a and b are integers respectively.

33. The method according to claim 32, wherein a = 2, b = 1, and c = 1, or where a = 1, b = 1, or c = 0.

34. The method according to claim 1, wherein the one or more directions include non - horizontal directions or non - vertical directions.

35. The method according to claim 1, wherein whether to calculate the gradient and / or which one or more directions of the gradient are to be calculated depends on one or more of the following: a luminance sample, an intra - prediction direction, or a mode of the luminance sample.

36. The method according to claim 1, wherein the way of calculating the gradient depends on the direction associated with the gradient.

37. The method according to claim 1, wherein the number of reference samples associated with the corresponding luminance block utilized in CCCM / CCLM depends on the direction.

38. The method according to claim 1, wherein at least one chrominance neighboring sample is used in the CCCM model.

39. The method according to claim 38, wherein the chrominance neighboring samples are adjacent or non - adjacent. Wherein the chrominance neighboring samples are represented as P( - n, y), P(x, - n), and P( - m, - n), and x and y respectively represent the horizontal position and the vertical position of the central sample relative to the upper - left coordinates of the video unit.

40. The method according to claim 39, wherein n = 1 or n = 2 and m = - 1 or m = - 2.

41. The method according to claim 38, wherein the chrominance neighboring samples are used together with position information.

42. The method according to claim 41, wherein (H - y)*P( - n, y) is used, where H represents the block height, P( - n, y) represents the chrominance neighboring sample, and y represents the vertical position of the central sample relative to the upper - left coordinates of the video unit, or wherein (W - x)*P(x, - n) is used, where W represents the block width, P(x, - n) represents the chrominance neighboring sample, and x represents the horizontal position of the central sample relative to the upper - left coordinates of the video unit.

43. The method according to claim 41, wherein (W – x + H - y)*P( - n, - n) is used, where W represents the block width, H represents the block height, P( - n, - n) represents the chrominance neighboring sample, and x and y respectively represent the horizontal position and the vertical position of the central sample relative to the upper - left coordinates of the video unit.

44. The method according to any one of claims 38 to 43, wherein the coefficient on the chrominance neighboring sample is a fixed value, or wherein the coefficient on the chrominance neighboring sample depends on the position of the chrominance neighboring sample, or wherein the coefficient on the chrominance neighboring sample is derived at the decoder.

45. The method according to claim 1, wherein one or more shapes are used in the CCCM model.

46. The method according to claim 45, wherein the determination of the one or more shapes used in the CCCM model is indicated in the bitstream, or wherein the determination of the one or more shapes used in the CCCM model is derived.

47. The method according to claim 45, wherein a rhombus shape with M1×N1 is used in the CCCM model, and wherein M1 represents the number of columns of samples, and N1 represents the number of rows of samples; and / or Among them, a cross shape with M2×N2 is used in the CCCM model, and wherein M2 represents the number of columns of samples, and N2 represents the number of rows of samples.

48. The method according to claim 47, wherein M1, N1, M2, and N2 depend on the color format.

49. The method according to claim 48, wherein for 4:4:4 and 4:2:0 color formats, M1 = N1.

50. The method according to claim 48, wherein for 4:4:4 and 4:2:0 color formats, M2 = N2.

51. The method according to claim 48, wherein for 4:2:2 color format, M1 < N1.

52. The method according to claim 48, wherein for the 4:2:2 color format, M2 < N2.

53. The method according to claim 47, wherein M1, N1, M2, and N2 depend on the coding and decoding information.

54. The method according to claim 53, wherein the coding and decoding information includes the resolution of the video.

55. The method according to claim 54, wherein at least one of M1 or N1 for a low-resolution video is less than at least one of M1 or N1 for a high-resolution video.

56. The method according to claim 54, wherein at least one of M2 or N2 for a low-resolution video is less than at least one of M2 or N2 for a high-resolution video.

57. The method according to claim 53, wherein the coding and decoding information includes syntax elements that are signaled at one of a sequence parameter set (SPS), a picture parameter set (PPS), a picture header, a picture, a sequence header, or a slice level.

58. The method according to claim 45, wherein whether to use the shape in the CCCM model is indicated at one of a sequence level, a group of pictures level, a picture level, a slice level, or a slice group level.

59. The method according to any one of claims 1 to 58, wherein the CCCM model using the gradient or the chrominance neighboring samples is used as an additional CCCM mode or to replace an existing CCCM mode.

60. The method according to any one of claims 1 to 59, wherein the indication of whether to determine and / or how to determine the gradient from the one or more directions associated with the video unit is indicated at one of the following: sequence level, group of pictures level, picture level, slice level, or slice group level.

61. The method according to any one of claims 1 to 59, wherein the indication of whether to determine and / or how to determine the gradient from the one or more directions associated with the video unit is indicated in one of the following: sequence header, picture header, sequence parameter set (SPS), video parameter set (VPS), dependency parameter set (DPS), decoding capability information (DCI), picture parameter set (PPS), adaptive parameter set (APS), slice header, or slice group header.

62. The method according to any one of claims 1 to 59, wherein the indication of whether to determine and / or how to determine the gradient from the one or more directions associated with the video unit is indicated in one of the following: prediction block (PB), transformation block (TB), coding and decoding block (CB), prediction unit (PU), transformation unit (TU), coding and decoding unit (CU), virtual pipeline data unit (VPDU), coding and decoding tree unit (CTU), CTU row, slice, tile, sub-picture, or a region containing more than one sample or pixel.

63. The method according to any one of claims 1 to 59, further comprising: Based on the encoding and decoding information of the video unit, whether and / or how to determine the gradients from the one or more directions associated with the video unit, the encoded and decoded information including at least one of the following: block size, color format, single and / or dual-tree partitioning, color component, slice type, or picture type.

64. A method for video processing, comprising: For the conversion between a video unit of a video and the bitstream of the video unit, applying a linear model (LM) mode to the video unit based on the non-downsampled luminance values; Determining a prediction of the video unit based on the LM mode; and Performing the conversion based on the prediction of the video unit.

65. The method according to claim 64, wherein for at least one of the following: 4:2:0, 4:2:2, or 4:4:4 color formats, the LM mode is applied based on non-downsampled luminance reconstruction samples.

66. The method according to claim 64, wherein the LM model is calculated based on non-downsampled luminance reconstruction samples adjacent to the video unit.

67. The method according to claim 64, wherein a gradient linear model (GLM) with a luminance value mode is applied based on non-downsampled luminance samples.

68. The method according to claim 67, wherein the predicted chroma sample points of the GLM having a luminance value pattern are calculated based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *G + a n+2 *B, where predChromaVal represents the predicted chroma sample points, G represents the type of gradient, B represents the offset, Y0, Y1, Y2, Y3, …, Yn respectively represent the non-downsampled luminance reconstruction sample point values, and a0, a1, a2, a3, …, a n respectively represent coefficients.

69. The method according to claim 64, wherein an indication of whether to use non-downsampled luminance samples is signaled based on a block-level syntax element.

70. The method according to claim 69, wherein the indication of whether to use non-downsampled luminance samples is signaled for a chrominance mode.

71. The method according to claim 70, wherein the chrominance mode includes one or more LM modes.

72. The method according to claim 69, wherein when the indication of whether to use non-downsampled luminance samples is not signaled, the non-downsampled luminance samples are presumed not to be used for the video unit.

73. The method according to claim 69, wherein the indication of whether to use non-downsampled luminance samples is signaled based on whether at least one of the LM-top-left (LM-TL), LM-left (LM-L), or LM-top (LM-T) modes is used for the video unit.

74. The method according to claim 69, wherein whether at least one of LM-TL, LM-L, or LM-T is used is based on whether non-downsampled luminance samples are used for the video unit.

75. The method according to claim 69, wherein the indication of whether to use non-downsampled luminance samples is signaled based on whether a multi-mode LM is used for the video unit.

76. The method according to claim 69, wherein whether to use a single-model LM mode or a multi-model LM mode is based on whether non-downsampled luminance samples are used for the video unit.

77. The method according to claim 64, wherein the chrominance prediction samples of the LM mode are derived based on non-downsampled luminance reconstruction samples within the current block.

78. The method according to claim 64, wherein a gradient and location based convolutional cross-component model (GL-CCCM) is applied based on the non-downsampled luminance samples.

79. The method according to claim 78, wherein the predicted chroma samples of the GL-CCCM are calculated based on predChromaVal = c0*C + c1*Gy + c2*Gx + c3*Y + c4*X + c5*P + c6*B, where predChroma-Val represents the predicted chroma samples, Gy and Gx respectively represent the vertical gradient and the horizontal gradient calculated from the non-downsampled luminance reconstruction sample values, Y and X parameters represent the vertical position and the horizontal position of the non-downsampled luminance samples relative to the upper left coordinates of the video unit, and c0, c1, c2, c3, c4, c5 and c6 respectively represent coefficients.

80. The method according to claim 64, wherein a variant of the LM mode is applied based on the non-downsampled luminance samples.

81. The method according to claim 80, wherein the predicted chroma sample of the variant of the LM mode is calculated based on predChromaVal = a0*Y0 + a1*Y1 + a2*Y2 + a3*Y3 + … + a n *Yn + a n+1 *B + a n+2 *L, where predChromaVal represents the predicted chroma sample, B represents an offset, L represents a non-linear or linear term, Y0, Y1, Y2, …, Yn respectively represent the upsampled luminance reconstruction sample values, and a0, a1, a2, a3, …, a n respectively represent coefficients.

82. The method according to claim 64, wherein whether to use the non-downsampled luminance samples for chroma encoding and decoding is derived based on the encoding and decoding information.

83. The method according to claim 82, wherein the encoding and decoding information includes at least one of the following: gradient histogram, color histogram or luminance sample values.

84. The method according to claim 64, wherein an indication of whether to use the non-downsampled luminance samples is signaled based on one of the following: SPS level syntax element, PPS level syntax element, PH level syntax element, Picture level syntax element, SH level syntax element, or Slice level syntax element.

85. The method according to claim 84, wherein the indication of whether to use the non-downsampled luminance samples is signaled for the chroma mode.

86. The method according to claim 85, wherein the chroma mode includes one or more LM modes.

87. The method according to any one of claims 64 to 86, wherein the LM mode includes at least one of the following: CCLM mode, Variant of the CCLM mode, CCCM mode, Variant of the CCCM mode, GL-CCCM mode, GLM mode, Variant of the GLM mode, GLM with luminance value, LM-L considering only the left neighbor, LM-T considering only the upper neighbor, LM-TL considering both the left neighbor and the upper neighbor, LM based on a single model, or LM based on multiple models.

88. The method according to any one of claims 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on the non-downsampled luminance values is indicated at one of the following: Sequence level, Group of pictures level, Picture level, Slice level, or Slice group level.

89. The method according to any one of claims 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on the non-downsampled luminance values is indicated in one of the following: Sequence header, Picture header, Sequence parameter set (SPS), Video Parameter Set (VPS), Dependency Parameter Set (DPS), Decoding Capability Information (DCI), Picture Parameter Set (PPS), Adaptive Parameter Set (APS), slice header, or slice group header.

90. The method according to any one of claims 64 to 86, wherein an indication of whether and / or how to apply the LM model to the video unit based on the non-downsampled luma values is included in one of the following: prediction block (PB), transform block (TB), codec block (CB), prediction unit (PU), transform unit (TU), codec unit (CU), virtual pipeline data unit (VPDU), codec tree unit (CTU), CTU row, slice, picture, sub-picture, or a region containing more than one sample or pixel.

91. The method according to any one of claims 64 to 86, further comprising: determining whether and / or how to apply the LM model to the video unit based on the non-downsampled luma values, based on the codec information of the video unit, the codec information including at least one of the following: block size, color format, single and / or dual tree segmentation, color component, slice type, or picture type.

92. The method according to any one of claims 1 to 91, wherein the transformation includes encoding the video unit into the bitstream.

93. The method according to any one of claims 1 to 91, wherein the transformation includes decoding the video unit from the bitstream.

94. An apparatus for video processing, comprising a processor and a non-transitory memory having instructions, wherein the instructions, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 93.

95. A non-transitory computer-readable storage medium storing instructions that cause a processor to perform the method according to any one of claims 1 to 93.

96. A non-transitory computer-readable recording medium storing a bitstream of a video, the bitstream of the video being generated by a method performed by an apparatus for video processing, wherein the method includes: determining gradients from one or more directions associated with a video unit of the video, wherein a Convolutional Cross-Component Model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; and generating the bitstream based on the prediction of the video unit.

97. A method for storing a bitstream of a video, comprising: determining gradients from one or more directions associated with a video unit of the video, wherein a Convolutional Cross-Component Model (CCCM) model is applied to the video unit; determining a prediction of the video unit by using the gradients from the one or more directions; generating the bitstream based on the prediction of the video unit; and storing the bitstream in a non-transitory computer-readable medium.

98. A non-transitory computer-readable recording medium stores a bitstream of a video, and the bitstream of the video is generated by a method executed by a device for video processing, where the method includes: Applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; Determining a prediction of the video unit based on the LM mode; And Generating the bitstream based on the prediction of the video unit.

99. A method for storing a bitstream of a video includes: Applying a linear model (LM) mode to a video unit of the video based on an upsampled luminance value; Determining a prediction of the video unit based on the LM mode; Generating the bitstream based on the prediction of the video unit; And Storing the bitstream in a non-transitory computer-readable medium.

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