Video image component prediction method and apparatus, computer storage medium

Abstract

This application provides a video image component prediction method and apparatus, and a computer storage medium. The method may include: acquiring a reference value set of a first image component of the current block; determining multiple first image component reference values ​​from the reference value set of the first image component; performing a first filtering process on the sample values ​​of the pixels corresponding to the multiple first image component reference values ​​respectively to obtain multiple filtered first image reference sample values; determining the image component reference value to be predicted corresponding to the multiple filtered first image reference sample values; determining the parameters of a component linear model based on the multiple filtered first image reference sample values ​​and the image component reference value to be predicted; performing a mapping process on the reconstructed value of the first image component of the current block according to the component linear model to obtain a mapping value; and determining the predicted value of the image component to be predicted of the current block based on the mapping value.

Description

[0001] Case Analysis

[0002] This application is a divisional application of Chinese Patent No. 201980041795.1, filed on October 11, 2019, entitled "Video Image Component Prediction Method and Apparatus, Computer Storage Medium". Technical Field

[0003] This application relates to the technical field of video encoding and decoding, and in particular to a video image component prediction method and apparatus, and a computer storage medium. Background Technology

[0004] As people's demands for video display quality increase, new video applications such as high-definition and ultra-high-definition video have emerged. With the increasing prevalence of high-resolution, high-quality video viewing applications, the requirements for video compression technology are also rising. H.265 / High Efficiency Video Coding (HEVC) is currently the latest international video compression standard. H.265 / HEVC's compression performance is about 50% higher than its predecessor, H.264 / Advanced Video Coding (AVC), but it still cannot meet the rapidly evolving needs of video applications, especially new video applications such as ultra-high-definition and virtual reality (VR).

[0005] The next-generation video coding standard, Versatile Video Coding (VVC), has integrated a prediction method based on a linear model into its coding tools. The chroma component can be predicted from the reconstructed luminance component through the linear model.

[0006] However, when predicting video components using a linear model, it is necessary to downsample the pixel values ​​in the luminance neighborhood and then find the maximum and minimum values ​​in the reference sample points obtained after downsampling to construct the linear model. Due to the large number of adjacent reference blocks, the complexity of model construction using the above method is high, resulting in low efficiency of chrominance prediction, which in turn affects the efficiency of video encoding and decoding. Summary of the Invention

[0007] This application provides a video image component prediction method and apparatus, and a computer storage medium, which can reduce the complexity of video component prediction, improve prediction efficiency, and thus improve video encoding and decoding efficiency.

[0008] The technical solution of this application embodiment can be implemented as follows:

[0009] This application provides a video component prediction method, including:

[0010] Obtain the reference value set of the first image component of the current block;

[0011] Multiple reference values ​​for the first image components are determined from the reference value set of the first image components;

[0012] The sample values ​​of the pixels corresponding to the plurality of first image component reference values ​​are subjected to a first filtering process to obtain a plurality of filtered first image reference sample values;

[0013] Determine the reference value of the image component to be predicted corresponding to the plurality of filtered first image reference samples, wherein the image component to be predicted is an image component that is different from the first image component;

[0014] Based on the multiple filtered first image reference samples and the reference values ​​of the image components to be predicted, the parameters of the component linear model are determined, wherein the component linear model represents the linear mapping relationship that maps the samples of the first image components to the samples of the image components to be predicted.

[0015] Based on the component linear model, the reconstructed value of the first image component of the current block is mapped to obtain the mapped value;

[0016] The predicted value of the image component to be predicted in the current block is determined based on the mapping value.

[0017] This application provides a video component prediction device, including:

[0018] The acquisition section is configured to acquire a reference value set for the first image component of the current block;

[0019] The determining part is configured to determine a plurality of first image component reference values ​​from the reference value set of the first image component;

[0020] The filtering section is configured to perform a first filtering process on the sample values ​​of the pixels corresponding to the plurality of first image component reference values ​​respectively, so as to obtain a plurality of filtered first image reference sample values.

[0021] The determining portion is further configured to determine a reference value for a to-be-predicted image component corresponding to the plurality of filtered first image reference samples, wherein the to-be-predicted image component is an image component different from the first image component; and to determine parameters of a component linear model based on the plurality of filtered first image reference samples and the reference value for the to-be-predicted image component, wherein the component linear model characterizes a linear mapping relationship that maps the sample value of the first image component to the sample value of the to-be-predicted image component.

[0022] The filtering section is further configured to map the reconstructed value of the first image component of the current block according to the component linear model to obtain a mapped value.

[0023] The prediction section is configured to determine the predicted value of the image component to be predicted for the current block based on the mapping value.

[0024] This application provides a video component prediction device, including:

[0025] Memory, used to store executable video component prediction instructions;

[0026] The processor is configured to execute executable video component prediction instructions stored in the memory to implement the video component prediction method provided in the embodiments of this application.

[0027] This application provides a computer-readable storage medium storing executable video component prediction instructions, which, when executed by a processor, implement the video component prediction method provided in this application.

[0028] This application provides a video image component prediction method. The video image component prediction device can, based on a directly acquired set of reference values ​​for the first image component corresponding to the current block, first select multiple first image component reference values, then perform filtering based on the pixel positions of the selected reference values ​​to obtain multiple filtered first image reference samples. Next, it finds the reference values ​​of the image component to be predicted corresponding to the multiple filtered first image reference samples, obtains the parameters of the component linear model, constructs the component linear model based on the parameters, and then uses the constructed component linear model to perform the prediction process for the image component to be predicted. Because multiple first image component reference values ​​are selected first during the component linear model construction process, and then filtering is performed based on the positions corresponding to the selected reference values ​​to construct the component linear model, the workload of filtering the pixels corresponding to the current block is saved, i.e., filtering operations are reduced, thereby reducing the complexity of constructing the component linear model, thus reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency. Attached Figure Description

[0029] Figure 1 This is a schematic diagram illustrating the relationship between the current block and adjacent reference pixels provided in an embodiment of this application.

[0030] Figure 2 An architecture diagram of a video image component prediction system provided in this application embodiment;

[0031] Figure 3AThis application provides a schematic block diagram of a video encoding system.

[0032] Figure 3B This application provides a schematic block diagram of a video decoding system according to an embodiment of the present application.

[0033] Figure 4 A flowchart of a video image component prediction method provided in this application embodiment Figure 1 ;

[0034] Figure 5 A flowchart of a video image component prediction method provided in this application embodiment Figure 2 ;

[0035] Figure 6 Flowchart 3 of a video image component prediction method provided in this application embodiment;

[0036] Figure 7 This is a schematic diagram of the structure of the prediction model based on the maximum and minimum values ​​provided in an embodiment of this application;

[0037] Figure 8 A schematic diagram of the structure of a video image component prediction device provided in this application embodiment. Figure 1 ;

[0038] Figure 9 A schematic diagram of the structure of a video image component prediction device provided in this application embodiment. Figure 2 . Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0041] Let's first introduce the concepts of intra-frame prediction and video encoding / decoding.

[0042] The main function of predictive encoding and decoding is to construct the predicted value of the current block using the existing reconstructed image in space or time during video encoding and decoding, and only transmit the difference between the original value and the predicted value, so as to reduce the amount of data transmitted.

[0043] The main function of intra-frame prediction is to construct the predicted value of the current block using the current block, the adjacent pixel units in the previous row, and the pixel units in the left column. For example... Figure 1 As shown, each pixel unit of the current block 101 is predicted using the recovered neighboring pixels around the current block 101 (i.e., the pixel units in the row 102 above the current block and the pixel units in the column 103 to the left of the current block).

[0044] In the embodiments of this application, for video images, three image components are typically used to represent processing blocks. These three image components are a luminance component, a blue chrominance component, and a red chrominance component. Specifically, the luminance component is typically represented by the symbol Y, the blue chrominance component is typically represented by the symbol Cb, and the red chrominance component is typically represented by the symbol Cr.

[0045] Currently, the commonly used sampling format for video images is YCbCr format, which includes:

[0046] The 4:4:4 format indicates that the blue or red chromaticity components are not downsampled; it takes four samples of the luminance component, four samples of the blue chromaticity component, and four samples of the red chromaticity component from every four consecutive pixels on each scan line.

[0047] The 4:2:2 format indicates that the luminance component is sampled horizontally at a ratio of 2:1 to the blue or red chrominance component, with no vertical downsampling. It takes 4 samples of the luminance component, 2 samples of the blue chrominance component, and 2 samples of the red chrominance component from every 4 consecutive pixels on each scan line.

[0048] 4:2:0 format: indicates that the luminance component is downsampled horizontally by a ratio of 2:1 and vertically by a ratio of 2:1 relative to the blue or red chrominance component; it takes two luminance component samples, one blue chrominance component sample, and one red chrominance component sample from every two consecutive pixels on the horizontal and vertical scan lines.

[0049] When the video image uses a YCbCr format of 4:2:0, if the luminance component of the video image is a processing block of size 2N×2N, then the corresponding blue or red chrominance component is a processing block of size N×N, where N is the side length of the processing block. In this embodiment, the following description uses the 4:2:0 format as an example, but the technical solution of this embodiment is also applicable to other sampling formats.

[0050] Based on the above concepts, this application provides a network architecture for a video encoding / decoding system that includes a video image component prediction method in intra-frame prediction. Figure 2This is a schematic diagram of the network architecture for video encoding and decoding in an embodiment of this application, as shown below. Figure 2 As shown, the network architecture includes one or more electronic devices 11 to 1N and a communication network 01, wherein the electronic devices 11 to 1N can perform video interaction through the communication network 01. The electronic devices can be various types of devices with video encoding and decoding capabilities, such as mobile phones, tablets, personal computers, personal digital assistants, navigators, digital phones, video phones, televisions, sensing devices, servers, etc., and this application embodiment is not limited to any particular type. The intra-frame prediction device in this application embodiment can be one of the aforementioned electronic devices.

[0051] The electronic device in this application embodiment has video encoding and decoding functions, and generally includes a video encoder and a video decoder.

[0052] For example, see Figure 3AAs shown, the video encoder 21 comprises the following structures: a transform and quantization unit 211, an intra-frame estimation unit 212, an intra-frame prediction unit 213, a motion compensation unit 214, a motion estimation unit 215, an inverse transform and inverse quantization unit 216, a filter control and analysis unit 217, a filtering unit 218, an entropy coding unit 219, and a decoded image buffer unit 210. The filtering unit 218 can implement deblocking filtering and Sample Adaptive Offset (SAO) filtering, while the entropy coding unit 219 can implement header information encoding and Context-based Adaptive Binary Arithmatic Coding (CABAC). For the input source video data, the encoding is performed using a coding tree. The partitioning of a Unit (CTU) yields a block to be encoded in the current video frame. After performing intra-frame prediction or inter-frame prediction on this block, the resulting residual information is transformed by the transform and quantization unit 211. This transformation includes converting the residual information from the pixel domain to the transform domain and quantizing the resulting transform coefficients to further reduce the bit rate. Intra-frame estimation unit 212 and intra-frame prediction unit 213 perform intra-frame prediction on the block to be encoded, for example, determining the intra-frame prediction mode for encoding the block. Motion compensation unit 214 and motion estimation unit 215 perform inter-frame prediction coding of the block to be encoded relative to one or more blocks in one or more reference frames to provide temporal prediction information. The motion estimation unit 215 estimates motion vectors, which can estimate the motion of the block to be encoded. The motion compensation unit 214 then performs motion compensation based on these motion vectors. After determining the intra-frame prediction mode, the intra-frame prediction unit... 213 is also used to provide the selected intra-frame prediction data to the entropy coding unit 219, and the motion estimation unit 215 also sends the calculated motion vector data to the entropy coding unit 219; in addition, the inverse transform and inverse quantization unit 216 is used to reconstruct the block to be encoded, reconstructing the residual block in the pixel domain. The reconstructed residual block is processed by the filter control analysis unit 217 and the filtering unit 218 to remove block artifacts, and then the reconstructed residual block is added to a predictive block in the frame of the decoding image buffer unit 210 to generate a reconstructed video coding block; the entropy coding unit 219 is used to encode various coding parameters and quantized transform coefficients. In the CABAC-based coding algorithm, the context content can be based on adjacent coding blocks and can be used to encode information indicating the determined intra-frame prediction mode, outputting the bitstream of the video data; while the decoding image buffer unit 210 is used to store the reconstructed video coding block for prediction reference. As video coding progresses, new reconstructed video coding blocks are continuously generated, and these reconstructed video coding blocks are stored in the decoding image buffer unit 210.

[0053] The video decoder 22 corresponding to the video encoder 21 has the following structure: Figure 3B As shown, it includes: an entropy decoding unit 221, an inverse transform and inverse quantization unit 222, an intra-frame prediction unit 223, a motion compensation unit 224, a filtering unit 225, and a decoded image buffer unit 226, etc. Among them, the entropy decoding unit 221 can perform header information decoding and CABAC decoding, and the filtering unit 225 can perform deblocking filtering and SAO filtering. The input video signal is processed... Figure 3A After encoding, the video signal bitstream is output. This bitstream is input to the video decoder 22, first passing through the entropy decoding unit 221 to obtain the decoded transform coefficients. These transform coefficients are then processed by the inverse transform and inverse quantization unit 222 to generate residual blocks in the pixel domain. The intra-frame prediction unit 223 can generate prediction data for the current decoded block based on the determined intra-frame prediction mode and data from previously decoded blocks in the current frame or image. The motion compensation unit 224 determines the prediction information for the current decoded block by analyzing motion vectors and other associated syntax elements, and uses this information... Predictive information is used to generate a predictive block for the current decoded block being decoded; a decoded video block is formed by summing the residual block from the inverse transform and inverse quantization unit 222 with the corresponding predictive block generated by the intra-prediction unit 223 or the motion compensation unit 224; the decoded video block is passed through the filtering unit 225 to remove block artifacts, thereby improving video quality; then the decoded video block is stored in the decoded image buffer unit 226, which stores reference images for subsequent intra-prediction or motion compensation, and is also used for the output display of the video signal.

[0054] Based on this, the technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. The video image component prediction method provided in the embodiments of this application is a prediction method in the intra-frame prediction process of prediction encoding and decoding. It can be applied to both the video encoder 21 and the video decoder 22, and the embodiments of this application do not specifically limit it.

[0055] In the next-generation video coding standard H.266, to further improve encoding and decoding performance and efficiency, cross-component prediction (CCP) has been extended and improved, introducing cross-component linear model prediction (CCLM). In H.266, CCLM implements predictions between the luma component and the blue chroma component, between the luma component and the red chroma component, and between the blue and red chroma components. The following description will focus on the existing CCLM as a background for video component prediction methods.

[0056] This application provides a video image component prediction method. The method is applied in a video image component prediction device. The function implemented by the method can be realized by the processor in the video image component prediction device calling program code. Of course, the program code can be stored in a computer storage medium. It can be seen that the video image component prediction device includes at least a processor and a storage medium.

[0057] Figure 4 This is a schematic diagram illustrating the implementation process of a video image component prediction method provided in an embodiment of this application, as shown below. Figure 4 As shown, the method includes:

[0058] S101. Obtain the reference value of the first image component of the current block;

[0059] S102. Determine multiple reference values ​​for the first image components from the reference value set of the first image components;

[0060] S103. Perform a first filtering process on the sample values ​​of the pixels corresponding to the multiple first image component reference values ​​to obtain multiple filtered first image reference sample values.

[0061] S104. Determine the reference values ​​of the image components to be predicted corresponding to the multiple filtered first image reference samples; wherein, the image components to be predicted are image components that are different from the first image components;

[0062] S105. Based on multiple filtered first image reference samples and the reference values ​​of the image components to be predicted, determine the parameters of the component linear model, wherein the component linear model represents the linear mapping relationship that maps the samples of the first image components to the samples of the image components to be predicted.

[0063] S106. Based on the component linear model, the reconstructed value of the first image component of the current block is mapped to obtain the mapped value.

[0064] S107. Determine the predicted value of the image component to be predicted for the current block based on the mapping value.

[0065] In S101, in this embodiment of the application, the current block is the coded block or decoded block to be predicted as an image component. In this embodiment of the application, the video image component prediction device obtains a first image component reference value for the current block, wherein the set of reference values ​​for the first image components includes one or more first image component reference values. The reference value for the current block can be obtained from a reference block, which can be a neighboring block or a non-neighboring block of the current block; this embodiment of the application does not impose any restrictions.

[0066] In some embodiments of this application, the video image component prediction apparatus determines one or more reference pixels located outside the current block and uses the one or more reference pixels as one or more first image component reference values.

[0067] It should be noted that, in the embodiments of this application, the adjacent processing blocks corresponding to the current block are processing blocks that are adjacent to one or more edges of the current block. One or more adjacent edges may include the adjacent top edge of the current block, the adjacent left edge of the current block, or the adjacent top edge and left edge of the current block. The embodiments of this application do not make specific limitations.

[0068] In some embodiments of this application, the video image component prediction device determines one or more reference pixels as adjacent pixels to the current block.

[0069] It should be noted that, in the embodiments of this application, one or more reference pixels can be adjacent pixels or non-adjacent pixels. This application does not impose any restrictions, and this application will use adjacent pixels as an example for illustration.

[0070] In this embodiment, one or more edge-adjacent pixels corresponding to adjacent processing blocks of the current block are used as one or more adjacent reference pixels of the current block. Each adjacent reference pixel corresponds to three image component reference values ​​(i.e., a first image component reference value, a second image component reference value, and a third image component reference value). Therefore, the video image component prediction device can obtain the reference value of the first image component in each of the one or more adjacent reference pixels corresponding to the current block, as a reference value set for the first image component. Here, one or more first image component reference values ​​are obtained, that is, one or more first image component reference values ​​represent the reference values ​​of the first image components corresponding to one or more adjacent pixels in the adjacent reference blocks of the current block. In this embodiment, the first image component is used to predict other image components.

[0071] In some embodiments of this application, the combination of the first image component and the image component to be predicted includes at least one of the following:

[0072] The first image component is the luminance component, and the image component to be predicted is either the first or second chrominance component; or...

[0073] The first image component is the first chromaticity component, and the image component to be predicted is either the luminance component or the second chromaticity component; or...

[0074] The first image component is the second chromaticity component, and the image component to be predicted is either the luminance component or the first chromaticity component; or...

[0075] The first image component is the first color component, and the image component to be predicted is either the second or third color component; or...

[0076] The first image component is the second color component, and the image component to be predicted is either the first color component or the third color component; or...

[0077] The first image component is the third color component, and the image component to be predicted is either the second color component or the first color component.

[0078] In some embodiments of this application, the first color component is the red component, the second color component is the green component, and the third color component is the blue component.

[0079] In this design, the first chromaticity component can be a blue chromaticity component, and the second chromaticity component can be a red chromaticity component. Alternatively, the first chromaticity component can be a red chromaticity component, and the second chromaticity component can be a blue chromaticity component. Here, the first chromaticity component and the second chromaticity component can represent the blue chromaticity component and the red chromaticity component, respectively.

[0080] Let's take the example where the first chromaticity component can be the blue chromaticity component and the second chromaticity component can be the red chromaticity component. If the first image component is the luminance component, and the image component to be predicted is the first chromaticity component, the video image component prediction device can use the luminance component to predict the blue chromaticity component; if the first image component is the luminance component, and the image component to be predicted is the second chromaticity component, the video image component prediction device can use the luminance component to predict the red chromaticity component; if the first image component is the first chromaticity component, and the image component to be predicted is the second chromaticity component, the video image component prediction device can use the blue chromaticity component to predict the red chromaticity component; if the first image component is the second chromaticity component, and the image component to be predicted is the first chromaticity component, the video image component prediction device can use the red chromaticity component to predict the blue chromaticity component.

[0081] In S102, the video image component prediction device can determine multiple first image component reference values ​​from one or more first image component reference values.

[0082] In some embodiments of this application, the video image component prediction device can compare one or more first image component reference values ​​contained in the reference value set of the first image component to determine the maximum first image component reference value and the minimum first image component reference value.

[0083] In some embodiments of this application, the video image component prediction device can determine the maximum and minimum values ​​among a plurality of first image component reference values ​​from one or more first image component reference values, and can determine a reference value that characterizes or represents the maximum or minimum of the first image component reference value from one or more first image component reference values.

[0084] For example, the video image component prediction device determines the maximum first image component reference value and the minimum first image component reference value from the reference value set of the first image component.

[0085] In the embodiments of this application, the video image component prediction device can use various methods to obtain the maximum first image component reference value and the minimum first image component reference value.

[0086] Method 1: Compare each of the first image component reference values ​​in one or more first image component reference values ​​in turn, and determine the first image component reference value with the largest first image component reference value and the smallest first image component reference value.

[0087] Method 2: Select at least two first image component reference values ​​at preset positions from one or more first image component reference values; divide the at least two first sub-image component reference values ​​into a set of maximum image component reference values ​​and a set of minimum image component reference values ​​according to their numerical values; obtain the maximum first image component reference value and the minimum first image component reference value based on the set of maximum image component reference values ​​and the set of minimum image component reference values.

[0088] In other words, in this embodiment, the video image component prediction device can select the largest value among one or more first image component reference values ​​as the largest first image component reference value, and select the smallest value among them as the smallest first image component reference value. The determination method can be a pairwise comparison or a sorting process; the specific determination method is not limited in this embodiment.

[0089] The video image component prediction device can further select several first image component reference values ​​corresponding to a preset position (preset pixel position) from the pixel positions corresponding to one or more first image component reference values, as at least two first image component reference values. Then, based on the at least two first image component reference values, it divides the data set into a maximum set (maximum image component reference value set) and a minimum set (minimum image component reference value set). Based on the maximum and minimum data sets, it determines the maximum and minimum first image component reference values. The process of determining the maximum and minimum first image component reference values ​​based on the maximum and minimum data sets can be achieved by averaging the maximum data set and averaging the minimum data set, respectively. Other methods can also be used to determine the maximum and minimum values; this embodiment does not impose limitations on these methods.

[0090] It should be noted that the number of values ​​in the largest and smallest data sets are integers greater than or equal to 1. The number of values ​​in the two sets may or may not be the same, and this application embodiment does not impose any restrictions.

[0091] The video image component prediction device can also take several first image component reference values ​​corresponding to a preset position as at least two first sub-image component reference values, and then directly select the maximum value from the at least two first sub-image component reference values ​​as the maximum first image component reference value, and select the minimum value as the minimum first image component reference value.

[0092] For example, the video image component prediction device divides the M largest first sub-image component reference values ​​(M can be a value greater than 4 or is not limited) from at least two first sub-image component reference values ​​into a set of maximum image component reference values, and divides the remaining M first sub-image component reference values ​​from at least two first sub-image component reference values ​​into a set of minimum image component reference values; finally, it performs mean processing on the set of maximum image component reference values ​​to obtain the maximum first image component reference value, and performs mean processing on the set of minimum image component reference values ​​to obtain the minimum first image component reference value.

[0093] It should be noted that, in the embodiments of this application, the maximum and minimum first image component reference values ​​can be determined directly by their numerical magnitude. Alternatively, they can be determined by first selecting first image component reference values ​​(at least two first sub-image component reference values) that can represent the validity of reference values ​​at preset positions, then dividing the valid first image component reference values ​​into a set with larger values ​​and a set with smaller values, and then determining the maximum first image component reference value based on the larger set and the minimum first image component reference value based on the smaller set; or by directly determining the maximum first image component reference value and the minimum first image component reference value from the set of valid first image component reference values ​​corresponding to preset positions by their numerical magnitude.

[0094] In the embodiments of this application, the video image component prediction device does not limit the method of determining the maximum first image component reference value and the minimum first image component reference value. For example, the video image component prediction device may divide one or more first image component reference values ​​into three or even four sets according to their size, process each set to obtain a representative parameter, and then select the maximum and minimum parameters from the representative parameters as the maximum first image component reference value and the minimum first image component reference value, etc.

[0095] In this embodiment of the application, the preset position can be a position representing the validity of the reference value of the first image component. The number of preset positions is not limited, for example, it can be 4, 6, etc.; the preset position can also be all the positions of adjacent pixels, which is not limited in this embodiment of the application.

[0096] For example, the preset position can be a preset number of first image component reference values ​​selected from both sides according to the sampling frequency based on the center of the row or column; or it can be the first image component reference value at other positions in the row or column after removing the edge point positions. This application embodiment does not impose any restrictions.

[0097] The allocation of preset positions in rows and columns can be either equal or according to a preset method; this application embodiment does not impose any restrictions. For example, when the number of preset positions is 4, and adjacent rows and adjacent columns are positions corresponding to one or more first image component reference values, 2 first image component reference values ​​corresponding to adjacent rows and 2 first image component reference values ​​corresponding to adjacent columns can be selected; alternatively, 1 first image component reference value can be selected from adjacent rows and 3 first image component reference values ​​from adjacent columns can be selected, etc. This application embodiment does not impose any restrictions.

[0098] The video image component prediction device can determine the maximum and minimum values ​​among one or more first image component reference values, thus obtaining the maximum value (maximum first image component reference value) and the minimum value (minimum first image component reference value). Alternatively, it can determine multiple reference values ​​from preset positions among the one or more first image component reference values, process them, and obtain the maximum first image component reference value with the largest characteristic value and the minimum first image component reference value with the smallest characteristic value. Here, in order to ensure that the sampling positions are consistent with or close to those of other video components, filtering is performed based on the pixel positions corresponding to the maximum and minimum first image component reference values ​​before subsequent processing.

[0099] In S103, the video image component prediction device performs a first filtering process on the sample values ​​of the pixels corresponding to the determined multiple first image component reference values ​​to obtain multiple filtered first image reference sample values.

[0100] In the embodiments of this application, the multiple filtered first image reference values ​​can be the filtered maximum first image component reference value and the filtered minimum first image component reference value, or they can be other multiple reference values ​​that include the maximum first image component reference value and the filtered minimum first image component reference value, or they can be other multiple reference values. The embodiments of this application do not impose any restrictions.

[0101] In this embodiment of the application, the video image component prediction device performs filtering (i.e., first filtering processing) on ​​the pixel position (i.e. the sample value of the corresponding pixel) corresponding to the determined first image component reference value, thereby obtaining multiple filtered first image reference samples. Thus, the component linear model can be constructed based on the multiple filtered first image reference samples.

[0102] In some embodiments of this application, the video image component prediction device performs a first filtering process on the sample values ​​of the pixels corresponding to the maximum first image component reference value and the minimum first image component reference value, respectively, to obtain the filtered maximum first image component reference value and the filtered minimum first image component reference value.

[0103] It should be noted that since the determined multiple first image component reference values ​​can be the maximum first image component reference value and the minimum first image component reference value, the filtering process can be performed on the pixel positions (i.e., the corresponding pixel sample values) used to determine the maximum and minimum first image component reference values ​​(i.e., the first filtering process). This will yield the corresponding filtered maximum and minimum first image component reference values ​​(i.e., multiple filtered first image reference sample values). Subsequently, the component linear model can be constructed based on the filtered maximum and minimum first image component reference values.

[0104] In this application embodiment, the filtering method can be upsampling, downsampling, low-pass filtering, etc., and this application embodiment does not limit it. The downsampling method can include mean, interpolation, or median, etc., and this application embodiment does not limit it.

[0105] In this embodiment, the first filtering process can be downsampling filtering and low-pass filtering.

[0106] For example, the video image component prediction device performs downsampling filtering on the pixel positions used to determine the maximum first image component reference value and the minimum first image component reference value, thereby obtaining the corresponding filtered maximum first image component reference value and filtered minimum first image component reference value.

[0107] The following explanation uses the mean as the basis for the sampling method.

[0108] The video component prediction device calculates the mean of the first image components for the region formed by the location corresponding to the maximum first image component reference value and its adjacent pixel locations. The pixels in this region are then fused into a single pixel. This mean value is the first image component reference value corresponding to the fused pixel, i.e., the filtered maximum first image component reference value. Similarly, the video component prediction device calculates the mean of the first image components for the region formed by the location corresponding to the minimum first image component reference value and its adjacent pixel locations. The pixels in this region are then fused into a single pixel. This mean value is the first image component reference value corresponding to the fused pixel, i.e., the filtered minimum first image component reference value.

[0109] It should be noted that, in the embodiments of this application, the downsampling process of the video image component prediction device is implemented using a filter. The specific determination of the range of vector pixel positions adjacent to the position corresponding to the maximum first image component reference value can be determined by the type of filter, and this embodiment of the application does not impose any restrictions.

[0110] In the embodiments of this application, the filter type can be a 6-tap filter or a 4-tap filter, and the embodiments of this application are not limited thereto.

[0111] In S104 and S105, the video image component prediction device determines reference values ​​for the image components to be predicted corresponding to multiple filtered first image reference samples, wherein the image components to be predicted are image components different from the first image components (e.g., second image components or third image components); then, based on the multiple filtered first image reference samples and the reference values ​​for the image components to be predicted, the device determines the parameters of the component linear model, wherein the component linear model characterizes the linear mapping relationship that maps the samples of the first image components to the samples of the image components to be predicted, such as a functional relationship.

[0112] In some embodiments of this application, the video image component prediction device determines the maximum image component reference value to be predicted corresponding to the maximum first image component reference value after filtering, and the minimum image component reference value to be predicted corresponding to the minimum first image component reference value after filtering.

[0113] It should be noted that in the embodiments of this application, the video image component prediction device can adopt the construction method of maximum and minimum values, and derive the model parameters (i.e. the parameters of the component linear model) according to the principle of "two points determine a line", and then construct the component linear model, that is, the simplified cross-component linear prediction model (CCLM).

[0114] In this embodiment, the video image component prediction device performs downsampling (i.e., filtering) to align with the position of the image to be predicted. This allows the determination of the reference value of the image component to be predicted corresponding to the filtered first image component reference sample value. For example, the maximum reference value of the image component to be predicted corresponding to the maximum filtered first image component reference value, and the minimum reference value of the image component to be predicted corresponding to the minimum filtered first image component reference value, are determined. Since the video image component prediction device has determined these two points (the maximum filtered first image component reference value and the maximum reference value of the image component to be predicted) and (the minimum filtered first image component reference value and the minimum reference value of the image component to be predicted), it can derive the model parameters based on the principle of "two points determine a line," thereby constructing a component linear model.

[0115] In some embodiments of this application, the video image component prediction device determines the parameters of the component linear model based on the filtered maximum first image component reference value, the maximum image component to be predicted reference value, the filtered minimum first image component reference value, and the minimum image component to be predicted reference value. The component linear model represents the linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the image component to be predicted.

[0116] In some embodiments of this application, the implementation method of the video image component prediction device determining the parameters of the component linear model based on the filtered maximum first image component reference value, the maximum image component reference value to be predicted, the filtered minimum first image component reference value, and the minimum image component reference value to be predicted may include: (1) the parameters of the component linear model further include a multiplicative factor and an additive offset. Thus, the video image component prediction device can calculate a first difference between the maximum image component reference value to be predicted and the minimum image component reference value to be predicted; calculate a second difference between the maximum first image component reference value and the minimum first image component reference value; set the multiplicative factor as the ratio of the first difference to the second difference; calculate a first product between the maximum first image component reference value and the multiplicative factor, and set the additive offset as the difference between the maximum image component reference value to be predicted and the first product; or, calculate a second product between the minimum first image component reference value and the multiplicative factor, and set the additive offset as the difference between the minimum image component reference value to be predicted and the second product. (2) Using the maximum first image component reference value after filtering, the maximum image component reference value to be predicted, and the preset initial linear model, construct the first sub-component linear model; using the minimum first image component reference value after filtering, the minimum image component reference value to be predicted, and the preset initial linear model, construct the second sub-component linear model; based on the first sub-component linear model and the second sub-component linear model, obtain the model parameters; using the model parameters and the preset initial linear model, construct the component linear model.

[0117] The values ​​mentioned above are determined or designed based on actual circumstances, and are not limited in the embodiments of this application.

[0118] For example, since the component linear model represents the linear mapping relationship between the first image component and the image component to be predicted, the video image component prediction device can predict the image component to be predicted based on the first image component and the component linear model. In the embodiments of this application, the image component to be predicted can be the chroma component.

[0119] For example, the component linear model can be represented by equation (1), as follows:

[0120] C=αY+β (1)

[0121] Where Y represents the reconstructed value of the first image component corresponding to a certain pixel in the current block (after downsampling), C represents the predicted value of the second image component corresponding to that pixel in the current block, and α and β are the model parameters of the above component linear model.

[0122] The specific implementation of the model parameters will be explained in detail in subsequent embodiments.

[0123] Understandably, the video image component prediction device can first select the maximum and minimum first image component reference values ​​based on one or more first image component reference values ​​corresponding to the current block obtained directly. Then, it can downsample the positions corresponding to the selected maximum and minimum first image component reference values ​​to construct a component linear model. This saves the workload of downsampling the pixels corresponding to the current block, i.e., reduces filtering operations, thereby reducing the complexity of constructing the component linear model, which in turn reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

[0124] In S106 and S107, in this embodiment of the application, after obtaining the component linear model, the video image component prediction device can directly use the component linear model to predict the video components of the current block, thereby obtaining the predicted values ​​of the image components to be predicted. Specifically, the video image component prediction device can perform mapping processing on the reconstructed values ​​of the first image component of the current block according to the component linear model to obtain mapping values, and then determine the predicted values ​​of the image components to be predicted in the current block based on the mapping values.

[0125] In some embodiments of this application, the video image component prediction device performs a second filtering process on the reconstructed value of the first image component to obtain a second filtered value of the reconstructed value of the first image component; and performs a mapping process on the second filtered value according to the component linear model to obtain a mapped value.

[0126] In some embodiments of this application, the video image component prediction device sets the mapping value to the predicted value of the image component to be predicted in the current block.

[0127] The second filtering process can be downsampling filtering or low-pass filtering.

[0128] In some embodiments of this application, the video image component prediction device may further perform a third filtering process on the mapping value setting to obtain a third filtered value of the mapping value; and set the third filtered value as the prediction value of the image component to be predicted in the current block.

[0129] The third filtering step can be a low-pass filter.

[0130] In the embodiments of this application, the predicted value represents the predicted value of the second image component or the predicted value of the third image component corresponding to one or more pixels of the current block.

[0131] It is understandable that, in the process of constructing the component linear model, multiple first image component reference values ​​are selected first, and then filtering is performed based on the positions corresponding to the selected multiple first image component reference values ​​to construct the component linear model. This saves the workload of filtering the pixels corresponding to the current block, that is, it reduces the filtering operation, thereby reducing the complexity of constructing the component linear model, which in turn reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

[0132] In some embodiments of this application, such as Figure 5 As shown, this embodiment of the invention also provides a video image component prediction method, including:

[0133] S201. Obtain the reference value set of the first image component of the current block;

[0134] S202. Compare the reference values ​​contained in the reference value set of the first image component to determine the maximum reference value and the minimum reference value of the first image component;

[0135] S203. Perform a first filtering process on the sample values ​​of the pixels corresponding to the maximum first image component reference value and the minimum first image component reference value respectively to obtain the filtered maximum first image component reference value and the filtered minimum first image component reference value.

[0136] S204. Determine the maximum image component reference value to be predicted corresponding to the maximum first image component reference value after filtering, and the minimum image component reference value to be predicted corresponding to the minimum first image component reference value after filtering.

[0137] S205. Based on the filtered maximum reference value of the first image component, the maximum reference value of the image component to be predicted, the filtered minimum reference value of the first image component, and the minimum reference value of the image component to be predicted, determine the parameters of the component linear model, wherein the component linear model represents the linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the image component to be predicted.

[0138] S206. Based on the component linear model, the reconstructed value of the first image component of the current block is mapped to obtain the mapped value.

[0139] S207. Based on the mapping value, determine the predicted value of the image component to be predicted in the current block.

[0140] In the embodiments of this application, the processes of S201-207 have been described in the preceding embodiments, and will not be repeated here.

[0141] It should be noted that when the video image component prediction device performs prediction, the reconstructed value of the first image component of the current block is obtained by filtering the first image component of the current block to obtain the reconstructed value of the first image component corresponding to the current block. Then, based on the component linear model and the reconstructed value of the first image component, the predicted value of the image component to be predicted of the current block is obtained.

[0142] In this embodiment, after obtaining the component linear model, the video image component prediction device needs to predict the predicted value of the image component to be predicted corresponding to each pixel in the current block, since the smallest unit for prediction of the current block is a pixel. Here, the video image component prediction first performs first image component filtering (e.g., downsampling) on ​​the current block to obtain the first image component reconstruction value corresponding to the current block, specifically, to obtain the first image component reconstruction value of each pixel in the current block.

[0143] In the embodiments of this application, the first image component reconstruction value represents the reconstruction value of the first image component corresponding to one or more pixels of the current block.

[0144] In this way, the video image component prediction device can map the reconstructed value of the first image component of the current block based on the component linear model to obtain the mapping value, and then obtain the predicted value of the image component to be predicted in the current block based on the mapping value.

[0145] In some embodiments of this application, such as Figure 6 As shown, the specific implementation of S204 can include: S2041-S2042, as follows:

[0146] S2041. Obtain the reference values ​​of the image components to be predicted for the current block;

[0147] S2042. Among the reference values ​​of the image components to be predicted, determine the maximum reference value of the image component to be predicted and the minimum reference value of the image component to be predicted.

[0148] In the embodiments of this application, during the process of constructing a component linear model based on the filtered maximum image component reference value and the filtered minimum image component reference value, the video image component prediction device, based on the principle of "two points determine a line", takes the first image component as the abscissa and the image component to be predicted as the ordinate. Given the values ​​of the abscissas of the two points, it is also necessary to determine the values ​​of the ordinates corresponding to the two points in order to determine a linear model, namely the component linear model, according to the principle of "two points determine a line".

[0149] In some embodiments of this application, the video image component prediction device converts the sampling point (Sample) position of the first image component reference value corresponding to the largest first image component reference value into a first sampling point position; sets the largest image component reference value to be predicted as a reference value located at the first sampling point position in the image component reference values ​​to be predicted; converts the sampling point position of the first image component reference value corresponding to the smallest first image component reference value into a second sampling point position; and sets the smallest image component reference value to be predicted as a reference value located at the second sampling point position in the image component reference values ​​to be predicted.

[0150] For example, taking a reference pixel as an adjacent pixel, the video image component prediction device can obtain one or more reference values ​​for the image component to be predicted corresponding to the current block based on the above description of adjacent blocks. Here, one or more reference values ​​for the image component to be predicted can refer to the reference value of the image component to be predicted in each of the one or more reference pixels corresponding to the current block, as a reference value for the image component to be predicted. In this way, the video image component prediction device obtains one or more reference values ​​for the image component to be predicted.

[0151] The video image component prediction device finds the first neighboring reference pixel containing the maximum filtered first image component reference value from among the pixels corresponding to one or more reference values ​​for the first image component to be predicted. It then uses the reference value for the first neighboring reference pixel as the maximum predicted image component reference value, thus determining the maximum predicted image component reference value corresponding to the maximum filtered first image component reference value. Furthermore, it finds the second neighboring reference pixel containing the minimum filtered first image component reference value from among the pixels corresponding to one or more reference values ​​for the first image component to be predicted. It then uses the reference value for the second neighboring reference pixel as the minimum predicted image component reference value, thus determining the minimum predicted image component reference value corresponding to the minimum filtered first image component reference value. Finally, based on the principle of "two points determine a line," a straight line is determined based on the two points: (maximum filtered first image component reference value, maximum predicted image component reference value) and (minimum filtered first image component reference value, minimum predicted image component reference value). The function (mapping relationship) represented by this straight line is the component linear model.

[0152] In some embodiments of this application, the video image component prediction device may first filter the positions of adjacent pixels to obtain one or more image component reference values ​​to be predicted for the filtered pixels, then find the first adjacent reference pixel where the maximum first image component reference value is located from the filtered pixel positions, and take the image component reference value to be predicted (one of one or more image component reference values ​​to be predicted) corresponding to the first adjacent reference pixel as the maximum image component reference value to be predicted, that is, determine the maximum image component reference value to be predicted corresponding to the maximum first image component reference value after filtering, and find the second adjacent reference pixel where the minimum first image component reference value is located from the filtered pixel positions, and take the image component reference value to be predicted corresponding to the second adjacent reference pixel as the minimum image component reference value to be predicted, that is, determine the minimum image component reference value to be predicted corresponding to the minimum first image component reference value after filtering.

[0153] It should be noted that the video image component prediction device can also perform filtering on adjacent pixel positions, which is a filtering process for the image component to be predicted, such as the chroma image component. This application embodiment does not impose any limitations. That is, in this application embodiment, the video image component prediction device can perform a fourth filtering process on the reference value of the image component to be predicted to obtain the reconstructed value of the image component to be predicted.

[0154] The fourth filtering step can be a low-pass filter.

[0155] In some embodiments of this application, the process of the video image component prediction device constructing a component linear model is as follows: using the filtered maximum first image component reference value, the maximum image component reference value to be predicted, and a preset initial linear model, a first sub-component linear model is constructed; using the filtered minimum first image component reference value, the minimum image component reference value to be predicted, and a preset initial linear model, a second sub-component linear model is constructed; based on the first sub-component linear model and the second sub-component linear model, model parameters are obtained; and using the model parameters and the preset initial linear model, a component linear model is constructed.

[0156] In this embodiment of the application, the preset initial linear model is an initial model with unknown model parameters.

[0157] For example, the initial linear model can be in the form of formula (1), but α and β are unknown. By constructing a binary second equation using the first subcomponent linear model and the second subcomponent linear model, the model parameters α and β can be solved. Substituting α and β into formula (1), the linear mapping relationship model between the first image component and the image component to be predicted can be obtained.

[0158] For example, by searching for the largest first image component reference value (the filtered largest first image component reference value) and the smallest first image component reference value (the filtered smallest first image component reference value), the model parameters are derived according to the principle of "two points determine a line", as shown in the following equation (2):

[0159]

[0160] Among them, L max and L min C represents the maximum and minimum values ​​found in the reference values ​​of the first image components corresponding to the unsampled left and / or top edges. max and C min L represents max and L min The reference values ​​of the image components to be predicted corresponding to the adjacent reference pixels at the corresponding positions. See also Figure 7 It shows a schematic diagram of the prediction model constructed based on the maximum and minimum values ​​of the current block; where the horizontal axis represents the reference value of the first image component of the current block, and the vertical axis represents the reference value of the image component to be predicted in the current block, according to L max and L min And C max and C min The model parameters α and β can be calculated using equation (2), and the constructed prediction model is C = αY + β. In the actual prediction process, Y represents the reconstructed value of the first image component corresponding to one of the pixels in the current block, and C represents the predicted value of the image component to be predicted corresponding to that pixel in the current block.

[0161] Understandably, the video image component prediction device can first select the maximum and minimum first image component reference values ​​based on one or more first image component reference values ​​corresponding to the current block obtained directly. Then, it can perform downsampling (filtering) based on the selected maximum first image component reference value and the position corresponding to the maximum first image component reference value, thereby constructing a component linear model. This saves the workload of downsampling the pixels corresponding to the current block, that is, it reduces the filtering operation, thereby reducing the complexity of constructing the component linear model, thus reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

[0162] Based on the foregoing embodiments, this application provides a video component prediction device, which includes various units and modules included in each unit. It can be implemented by a processor in the video component prediction device; of course, it can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit, a microprocessor, a digital signal processor (DSP), or a field-programmable gate array, etc.

[0163] like Figure 8 As shown, this application embodiment provides a video component prediction device 3, including:

[0164] Acquisition section 30 is configured to acquire a reference value set of the first image component of the current block, wherein the reference value set of the first image component contains one or more first image component reference values.

[0165] Determining part 31 is configured to determine a plurality of first image component reference values ​​from the reference value set of the first image component;

[0166] The filtering section 32 is configured to perform a first filtering process on the sample values ​​of the pixels corresponding to the plurality of first image component reference values ​​respectively to obtain a plurality of filtered first image reference sample values;

[0167] The determining portion 31 is further configured to determine a reference value for a to-be-predicted image component corresponding to the plurality of filtered first image reference samples, wherein the to-be-predicted image component is an image component different from the first image component; and to determine parameters of a component linear model based on the plurality of filtered first image reference samples and the reference value for the to-be-predicted image component, wherein the component linear model characterizes a linear mapping relationship that maps the sample value of the first image component to the sample value of the to-be-predicted image component.

[0168] The filtering section 32 is further configured to perform mapping processing on the reconstructed value of the first image component of the current block according to the component linear model to obtain the mapping value;

[0169] The prediction section 33 is configured to determine the predicted value of the image component to be predicted for the current block based on the mapping value.

[0170] In some embodiments of this application, the determining portion 31 is further configured to compare the reference values ​​contained in the reference value set of the first image component to determine the maximum first image component reference value and the minimum first image component reference value.

[0171] In some embodiments of this application, the filtering section 32 is further configured to perform the first filtering process on the sample values ​​of the pixels corresponding to the maximum first image component reference value and the minimum first image component reference value, respectively, to obtain the filtered maximum first image component reference value and the filtered minimum first image component reference value.

[0172] In some embodiments of this application, the determining portion 31 is further configured to determine the maximum image component reference value to be predicted corresponding to the maximum first image component reference value after filtering, and the minimum image component reference value to be predicted corresponding to the minimum first image component reference value after filtering.

[0173] In some embodiments of this application, the determining part 31 is further configured to determine the parameters of the component linear model based on the filtered maximum first image component reference value, the maximum image component reference value to be predicted, the filtered minimum first image component reference value, and the minimum image component reference value to be predicted, wherein the component linear model characterizes a linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the image component to be predicted.

[0174] In some embodiments of this application, the determining portion 31 is further configured to determine one or more reference pixels located outside the current block;

[0175] The acquisition portion 30 is further configured to use the one or more reference pixels as reference values ​​for the one or more first image components.

[0176] In some embodiments of this application, the determining portion 31 is further configured to determine the pixel adjacent to the current block as one or more reference pixels.

[0177] In some embodiments of this application, the filtering section 32 is further configured to perform a second filtering process on the reconstructed value of the first image component to obtain a second filtered value of the reconstructed value of the first image component; and to perform a mapping process on the second filtered value according to the component linear model to obtain the mapped value.

[0178] In some embodiments of this application, the second filtering process is downsampling filtering or low-pass filtering.

[0179] In some embodiments of this application, the prediction portion 33 is further configured to set the mapping value as the predicted value of the image component to be predicted in the current block.

[0180] In some embodiments of this application, the filtering section 32 is further configured to perform a third filtering process on the mapping value setting to obtain a third filtered value of the mapping value;

[0181] The prediction section 33 is further configured to set the third filter value as the predicted value of the image component to be predicted in the current block.

[0182] In some embodiments of this application, the third filtering process is a low-pass filter.

[0183] In some embodiments of this application, the determining portion 31 is further configured to obtain a reference value of the image component to be predicted for the current block; and to determine the maximum reference value of the image component to be predicted and the minimum reference value of the image component to be predicted from the reference values ​​of the image component to be predicted.

[0184] In some embodiments of this application, the filtering section 32 is further configured to perform a fourth filtering process on the reference value of the image component to be predicted to obtain the reconstructed value of the image component to be predicted.

[0185] In some embodiments of this application, the fourth filtering process is a low-pass filter.

[0186] In some embodiments of this application, the determining portion 31 is further configured to convert the sampling point position of the first image component reference value corresponding to the maximum first image component reference value into a first sampling point position; set the maximum image component reference value to be predicted into a reference value located at the first sampling point position in the image component reference values ​​to be predicted; convert the sampling point position of the first image component reference value corresponding to the minimum first image component reference value into a second sampling point position; and set the minimum image component reference value to be predicted into a reference value located at the second sampling point position in the image component reference values ​​to be predicted.

[0187] In some embodiments of this application, the determining part 31 is further configured to construct a first sub-component linear model using the filtered maximum first image component reference value, the maximum image component reference value to be predicted, and a preset initial linear model; construct a second sub-component linear model using the filtered minimum first image component reference value, the minimum image component reference value to be predicted, and the preset initial linear model; obtain model parameters based on the first sub-component linear model and the second sub-component linear model; and construct the component linear model using the model parameters and the preset initial linear model.

[0188] In some embodiments of this application, the determining portion 31 is further configured such that the parameters of the component linear model include a multiplicative factor and an additive offset; calculates a first difference between the maximum reference value of the image component to be predicted and the minimum reference value of the image component to be predicted; calculates a second difference between the maximum reference value of the first image component and the minimum reference value of the first image component; sets the multiplicative factor as the ratio of the first difference to the second difference; calculates a first product between the maximum reference value of the first image component and the multiplicative factor, and sets the additive offset as the difference between the maximum reference value of the image component to be predicted and the first product; or, calculates a second product between the minimum reference value of the first image component and the multiplicative factor, and sets the additive offset as the difference between the minimum reference value of the image component to be predicted and the second product.

[0189] In some embodiments of this application, the first image component is a luminance component, and the image component to be predicted is a first or a second chrominance component; or,

[0190] The first image component is the first chroma component, and the image component to be predicted is the luminance component or the second chroma component; or...

[0191] The first image component is the second chroma component, and the image component to be predicted is either the luminance component or the first chroma component; or...

[0192] The first image component is a first color component, and the image component to be predicted is a second color component or a third color component; or,

[0193] The first image component is the second color component, and the image component to be predicted is either the first color component or the third color component; or...

[0194] The first image component is the third color component, and the image component to be predicted is either the second color component or the first color component.

[0195] In some embodiments of this application, the first color component is a red component, the second color component is a green component, and the third color component is a blue component.

[0196] In some embodiments of this application, the first filtering process is downsampling filtering or low-pass filtering.

[0197] It should be noted that, in the embodiments of this application, if the above-described video component prediction method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a mobile phone, tablet computer, personal computer, personal digital assistant, navigator, digital phone, video phone, television, sensor device, server, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0198] In practical applications, such as Figure 9 As shown, this application embodiment provides a video component prediction device, including:

[0199] Memory 34 is used to store executable video component prediction instructions;

[0200] The processor 35 is configured to execute the executable video component prediction instructions stored in the memory 34 to implement the steps in the video component prediction method provided in the above embodiments.

[0201] Accordingly, this application provides a computer-readable storage medium storing video component prediction instructions thereon, which, when executed by processor 35, implement the steps in the video component prediction method provided in the above embodiments.

[0202] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0203] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0204] Industrial applicability

[0205] In this embodiment, the video component prediction device can first determine multiple first image component reference values ​​based on the directly acquired reference value set of the first image component corresponding to the current block, and then perform filtering processing according to the positions corresponding to the determined multiple first image component reference values ​​to construct a component linear model. This saves the workload of filtering the pixels corresponding to the current block, that is, reduces the filtering operation, thereby reducing the complexity of constructing the component linear model, thereby reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

Claims

1. An image component prediction method, applied to a decoder, comprising: Determine the reference sample set for the first image component of the current block; Multiple reference samples for the first image component are determined from the reference sample set of the first image component according to a preset position; A first filtering process is performed on the plurality of first image component reference samples to determine a plurality of filtered first image reference samples; Determine the image component reference sample to be predicted corresponding to the plurality of filtered first image reference samples, wherein the image component to be predicted is an image component different from the first image component; Based on the multiple filtered first image reference samples and the image component reference samples to be predicted, the parameters of the component model are determined, wherein the component model represents the mapping relationship that maps the samples of the first image component to the samples of the image component to be predicted. According to the component model, the reconstructed samples of the first image component of the current block are mapped to obtain mapped samples; The predicted value of the image component to be predicted in the current block is determined based on the mapping sample; The step of determining multiple first image component reference samples from the reference sample set of the first image component according to preset positions includes: Four preset positions are determined; Based on the four preset positions, the plurality of first image component reference samples are determined from samples on one or more adjacent rows above the current block, and / or from samples on one or more adjacent columns to the left of the current block.

2. The method according to claim 1, characterized in that, The reference sample set for determining the first image component of the current block includes: The reference sample set for the first image component is determined based on the neighboring samples of the current block.

3. The method according to claim 1, characterized in that, The step of determining multiple first image component reference samples from the reference sample set of the first image component according to preset positions includes: If all four preset positions are located on the top side of the current block, then the plurality of first image component reference samples are determined from samples in one or more adjacent rows on the top side of the current block according to the four preset positions; If all four preset positions are located to the left of the current block, then the plurality of first image component reference samples are determined from samples in one or more adjacent columns to the left of the current block based on the four preset positions. If two of the four preset positions are located on the top side of the current block, and the other two of the four preset positions are located on the left side of the current block, then the plurality of first image component reference samples are determined from samples on one or more adjacent rows on the top side of the current block based on the two preset positions on the top side of the current block, and from samples on one or more adjacent columns on the left side of the current block based on the two preset positions on the left side of the current block.

4. The method according to claim 1, characterized in that, The step of determining the parameters of the component model based on the plurality of filtered first image reference samples and the image component reference samples to be predicted includes: Based on the plurality of filtered first image reference samples, determine the filtered maximum first image component reference sample and the filtered minimum first image component reference sample; Determine the maximum image component reference sample to be predicted corresponding to the maximum first image component reference sample after filtering, and the minimum image component reference sample to be predicted corresponding to the minimum first image component reference sample after filtering. The parameters of the component model are determined based on the filtered maximum first image component reference sample, the maximum image component reference sample to be predicted, the filtered minimum first image component reference sample, and the minimum image component reference sample to be predicted.

5. The method according to any one of claims 1 to 4, characterized in that, The step of mapping the reconstructed samples of the first image component of the current block according to the component model to obtain mapped samples includes: The reconstructed samples of the first image component are subjected to a second filtering process to obtain the second filtered samples of the reconstructed samples of the first image component. Based on the component model, the second filtered sample is mapped to obtain the mapped sample.

6. An image component prediction method, applied to an encoder, comprising: Determine the reference sample set for the first image component of the current block; Multiple reference samples for the first image component are determined from the reference sample set of the first image component according to a preset position; A first filtering process is performed on the plurality of first image component reference samples to determine a plurality of filtered first image reference samples; Determine the image component reference sample to be predicted corresponding to the plurality of filtered first image reference samples, wherein the image component to be predicted is an image component different from the first image component; Based on the multiple filtered first image reference samples and the image component reference samples to be predicted, the parameters of the component model are determined, wherein the component model represents the mapping relationship that maps the samples of the first image component to the samples of the image component to be predicted. According to the component model, the reconstructed samples of the first image component of the current block are mapped to obtain mapped samples; The predicted value of the image component to be predicted in the current block is determined based on the mapping sample; The step of determining multiple first image component reference samples from the reference sample set of the first image component according to preset positions includes: Four preset positions are determined; Based on the four preset positions, the plurality of first image component reference samples are determined from samples on one or more adjacent rows above the current block, and / or from samples on one or more adjacent columns to the left of the current block.

7. The method according to claim 6, characterized in that, The reference sample set for determining the first image component of the current block includes: The reference sample set for the first image component is determined based on the neighboring samples of the current block.

8. The method according to claim 6, characterized in that, The step of determining multiple first image component reference samples from the reference sample set of the first image component according to preset positions includes: If all four preset positions are located on the top side of the current block, then the plurality of first image component reference samples are determined from samples in one or more adjacent rows on the top side of the current block according to the four preset positions; If all four preset positions are located to the left of the current block, then the plurality of first image component reference samples are determined from samples in one or more adjacent columns to the left of the current block based on the four preset positions. If two of the four preset positions are located on the top side of the current block, and the other two of the four preset positions are located on the left side of the current block, then the plurality of first image component reference samples are determined from samples on one or more adjacent rows on the top side of the current block based on the two preset positions on the top side of the current block, and from samples on one or more adjacent columns on the left side of the current block based on the two preset positions on the left side of the current block.

9. The method according to claim 6, characterized in that, The step of determining the parameters of the component model based on the plurality of filtered first image reference samples and the image component reference samples to be predicted includes: Based on the plurality of filtered first image reference samples, determine the filtered maximum first image component reference sample and the filtered minimum first image component reference sample; Determine the maximum image component reference sample to be predicted corresponding to the maximum first image component reference sample after filtering, and the minimum image component reference sample to be predicted corresponding to the minimum first image component reference sample after filtering. The parameters of the component model are determined based on the filtered maximum first image component reference sample, the maximum image component reference sample to be predicted, the filtered minimum first image component reference sample, and the minimum image component reference sample to be predicted.

10. The method according to any one of claims 6 to 9, characterized in that, The step of mapping the reconstructed samples of the first image component of the current block according to the component model to obtain mapped samples includes: The reconstructed samples of the first image component are subjected to a second filtering process to obtain the second filtered samples of the reconstructed samples of the first image component. Based on the component model, the second filtered sample is mapped to obtain the mapped sample.

11. An encoder, characterized in that, The encoder includes: The determining part is configured to determine a reference sample set of the first image component of the current block; and to determine a plurality of first image component reference samples from the reference sample set of the first image component according to a preset position; The filtering section is configured to perform a first filtering process on the plurality of first image component reference samples to determine a plurality of filtered first image reference samples. The determining part is further configured to determine a reference sample of the image component to be predicted corresponding to the plurality of filtered first image reference samples, wherein the image component to be predicted is an image component different from the first image component; and to determine the parameters of the component model based on the plurality of filtered first image reference samples and the reference sample of the image component to be predicted, wherein the component model represents the mapping relationship that maps the samples of the first image component to the samples of the image component to be predicted. The filtering section is further configured to perform mapping processing on the reconstructed samples of the first image component of the current block according to the component model to obtain mapped samples; The prediction section is configured to determine the predicted value of the image component to be predicted for the current block based on the mapping sample; The determining part is further configured to determine four preset positions; and based on the four preset positions, determine the plurality of first image component reference samples from samples on one or more adjacent rows above the current block, and / or from samples on one or more adjacent columns to the left of the current block.

12. An encoder, characterized in that, include: Memory, used to store executable video component prediction instructions; A processor, when executing executable video component prediction instructions stored in the memory, implements the method of any one of claims 6 to 10.

13. A decoder, characterized in that, The decoder includes: The determining part is configured to determine a reference sample set of the first image component of the current block; and to determine a plurality of first image component reference samples from the reference sample set of the first image component according to a preset position; The filtering section is configured to perform a first filtering process on the plurality of first image component reference samples to determine a plurality of filtered first image reference samples. The determining part is further configured to determine a reference sample of the image component to be predicted corresponding to the plurality of filtered first image reference samples, wherein the image component to be predicted is an image component different from the first image component; and to determine the parameters of the component model based on the plurality of filtered first image reference samples and the reference sample of the image component to be predicted, wherein the component model represents the mapping relationship that maps the samples of the first image component to the samples of the image component to be predicted. The filtering section is further configured to perform mapping processing on the reconstructed samples of the first image component of the current block according to the component model to obtain mapped samples; The prediction section is configured to determine the predicted value of the image component to be predicted for the current block based on the mapping sample; The determining part is further configured to determine four preset positions; and based on the four preset positions, determine the plurality of first image component reference samples from samples on one or more adjacent rows above the current block, and / or from samples on one or more adjacent columns to the left of the current block.

14. A decoder, characterized in that, include: Memory, used to store executable video component prediction instructions; A processor, when executing executable video component prediction instructions stored in the memory, implements the method of any one of claims 1 to 5.

15. A computer-readable storage medium, characterized in that, It stores executable video component prediction instructions for causing a processor to execute, thereby implementing the method of any one of claims 1 to 5, or the method of any one of claims 6 to 10.

16. A computer storage medium storing executable video component prediction instructions and a bitstream thereon, wherein, When the executable video component prediction instruction is executed by the processor, it implements the steps of the method as described in any one of claims 6 to 10 to generate the bitstream.

17. A method for transmitting a code stream, characterized in that, Generate a bitstream by performing the method as described in any one of claims 6 to 10; and transmit the bitstream.

Images