Video image component prediction method and apparatus, computer storage medium

Abstract

To provide a video picture component prediction method and device, capable of improving the video coding and decoding efficiency.SOLUTION: A video picture component prediction method includes: determining, from a reference value set of a first picture component of a current block, multiple first picture component reference values; respectively carrying out first filtering processing on a sample value of a pixel point corresponding to each of the multiple first picture component reference values to determine reference values, corresponding to the multiple filtered first picture reference sample values acquired, of a picture component to be predicted; based on the multiple filtered first picture reference sample values and the reference values of a picture component to be predicted, determining a parameter of a component linear model; based on the component linear model, carrying out mapping processing on a reconstruction value of the first picture component of the current block to obtain a mapping value; and based on the mapping value, determining a prediction value of the picture component to be predicted of the current block.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The embodiments of the present application relate 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]

[0002] As people's demands for video display quality increase, new video applications such as high-definition and ultra-high-definition video have emerged. As these high-resolution, high-quality video viewing applications continue to expand, the requirements for video compression technology are also increasing. H.265 / High Efficiency Video Coding (HEVC) is the current latest international video compression standard, and although it offers approximately 50% improvement over the previous generation video coding standard, H.264 / Advanced Video Coding (AVC), it still cannot meet the needs of the rapid development of video applications, especially new video applications such as ultra-high-definition video and virtual reality (VR).

[0003] The coding tool adopted in the next-generation video coding standard, Versatile Video Coding (VVC), employs a linear model-based prediction method, in which the chrominance predicted value of the chrominance component can be obtained by the luma component reconstructed through a linear model.

[0004] However, when predicting video components using a linear model, after performing downsampling using pixel values ​​in the neighboring luminance region, it is necessary to find the maximum and minimum values ​​from the reference sample points obtained by downsampling to construct a linear model. Because there are a large number of neighboring reference blocks, the complexity of constructing a model using the above method is high. As a result, the efficiency of chrominance prediction is low, which affects the efficiency of video encoding and decoding. Summary of the Invention

[0005] Embodiments of the present application provide a video image component prediction method and apparatus, and a computer storage medium, which can reduce the complexity of video component prediction and improve prediction efficiency, thereby improving video encoding and decoding efficiency.

[0006] The technical solutions of the embodiments of the present application can be realized as follows:

[0007] An embodiment of the present application provides a video component prediction method, the method comprising: Obtaining a set of reference values ​​for a first image component of the current block; determining a plurality of first image component reference values ​​from the set of first image component reference values; performing a first filtering process on the sample values ​​of the pixel points corresponding to the plurality of first image component reference values, respectively, to obtain a plurality of filtered first image reference sample values; determining a prediction-to-be-predicted image component reference value corresponding to the plurality of filtered first image reference sample values, wherein the prediction-to-be-predicted image component is an image component different from the first image component; determining parameters of a component linear model based on the plurality of filtered first image reference sample values ​​and the prediction-to-be-predicted image component reference values, wherein the component linear model represents a linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the prediction-to-be-predicted image component; performing a mapping operation on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapped value; determining a predicted value of the prediction-to-be-predicted image component of the current block based on the mapping value.

[0008] An embodiment of the present application provides a video content prediction device, the device comprising: an acquisition unit configured to acquire a set of reference values ​​for a first image component of a current block; and a determination unit configured to determine a plurality of first image component reference values ​​from the set of reference values ​​for the first image component. a filtering unit configured to perform a first filtering process on sample values ​​of pixel points corresponding to the plurality of first image component reference values, respectively, to obtain a plurality of filtered first image reference sample values; a prediction unit configured to determine a prediction value of the prediction-to-be-predicted image component of the current block based on the mapping value; the determination unit is further configured to determine image component reference values ​​to be predicted corresponding to the plurality of filtered first image reference sample values, the image component to be predicted being an image component different from the first image component, and determine parameters of a component linear model based on the plurality of filtered first image reference sample values ​​and the image component reference values ​​to be predicted, the component linear model representing a linear mapping relationship that maps sample values ​​of the first image component to sample values ​​of the image component to be predicted; The filtering unit is further configured to perform a mapping operation on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapping value.

[0009] An embodiment of the present application provides a video content prediction device, the device comprising: a memory configured to store executable video component prediction instructions; a processor configured to implement a video component prediction method according to an embodiment of the present application when executing executable video component prediction instructions stored in the memory.

[0010] An embodiment of the present application provides a computer-readable storage medium including executable video component prediction instructions, the executable video component prediction instructions being configured to implement a video component prediction method according to an embodiment of the present application when executed by a processor.

[0011] In an embodiment of the present application, a video image component prediction method is provided, in which a video image component prediction device first selects a plurality of first image component reference values ​​based on a set of directly obtained first image component reference values ​​corresponding to a current block, then performs a filtering process based on pixel point positions of the selected plurality of first image component reference values ​​to obtain a plurality of filtered first image reference sample values, then finds image component reference values ​​to be predicted corresponding to the filtered plurality of first image reference sample values ​​to obtain component linear model parameters, constructs a component linear model based on the component linear model parameters, and then performs a prediction process for the image component to be predicted using the constructed component linear model. In the component linear model construction process, first selects a plurality of first image component reference values, then performs a filtering process based on positions corresponding to the selected plurality of first image component reference values ​​to construct the component linear model, thereby reducing the workload of the filtering process for pixel points corresponding to the current block, i.e., reducing the filtering operation, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 2 is a schematic diagram illustrating the relationship between a current block and neighboring reference pixel points according to an embodiment of the present application; [Figure 2] 1 is an architecture diagram of a video image component prediction system according to an embodiment of the present application; [Figure 3A] 1 is a schematic block diagram of a video encoding system according to an embodiment of the present application; [Figure 3B] 1 is a schematic block diagram of a video decoding system according to an embodiment of the present application; [Figure 4] 1 is a flowchart 1 of a video image component prediction method in an embodiment of the present application; [Figure 5] 2 is a flowchart 2 of a video image component prediction method in an embodiment of the present application; [Figure 6]3 is a flowchart 3 of a video image component prediction method in an embodiment of the present application; [Figure 7] FIG. 1 is a structural diagram for constructing a prediction model based on maximum and minimum values ​​according to an embodiment of the present application; [Figure 8] 1 is a schematic structural diagram 1 of a video image component prediction device according to an embodiment of the present application; [Figure 9] 2 is a schematic structural diagram 2 of a video image component prediction device according to an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in more detail below with reference to the drawings. The embodiments described below are not intended to limit the present application, and all other embodiments that can be obtained without the creative efforts of those skilled in the art are included in the protection scope of the present application.

[0014] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. The terms used herein are used only to describe the embodiments of the present application and are not intended to limit the present application.

[0015] Below, we will first explain the concepts of intra-frame prediction, video encoding and decoding, etc.

[0016] The main function of predictive coding and decoding in video coding and decoding is to generate a predicted value for the current block using an existing reconstructed image in space or time, and then transmit only the difference between the original value and the predicted value, thereby achieving the goal of reducing the amount of data transmitted.

[0017] The main function of intra-frame prediction is to generate a predicted value for a current block using a current block and pixel units in one row above and one column to the left of the current block. As shown in Figure 1, each pixel unit of the current block 101 is predicted using the reconstructed neighboring pixels around the current block 101 (i.e., pixel units in the top row 102 and the left column 103 adjacent to the current block).

[0018] In the present embodiment, for a video image, three image components are typically used to represent a processing block, where the three image components are a luminance component, a blue color difference component, and a red color difference component, respectively. Specifically, the luminance component is typically represented by the symbol Y, the blue color difference component is typically represented by the symbol Cb, and the red color difference component is typically represented by the symbol Cr.

[0019] Currently, the common sampling format of video images is the YCbCr format, which includes the following formats:

[0020] 4:4:4 format: means that the blue-difference or red-difference components are not downsampled, which means that for every four consecutive pixel points on each scan line, four samples of the luma component, four samples of the blue-difference component, and four samples of the red-difference component are extracted.

[0021] 4:2:2 format: means that the luma component is horizontally sampled 2:1 relative to the blue-difference or red-difference components, and no vertical downsampling is performed. This means that for every four consecutive pixel points on each scan line, four samples of the luma component, two samples of the blue-difference component, and two samples of the red-difference component are extracted.

[0022] 4:2:0 format: means that the luminance component is horizontally sampled at 2:1 with respect to the blue-difference or red-difference component, and downsampled vertically at 2:1, which means that for every two consecutive pixel points in a horizontal scan line and a vertical scan line, two samples of the luminance component, one sample of the blue-difference component, and one sample of the red-difference component are extracted.

[0023] Under the condition that the video image adopts YCbCr in 4:2:0 format, when the luminance component of the video image is a processing block of 2N×2N size, the corresponding blue color difference component or red color difference component is a processing block of N×N size, where N is the side length of the processing block. In the embodiments of the present application, the 4:2:0 format is taken as an example for description below, but the technical solutions of the embodiments of the present application are also applicable to other sampling formats.

[0024] Based on the above concept, an embodiment of the present application provides a network architecture for a video encoding / decoding system including a video image component prediction method for intra-frame prediction. FIG. 2 is a structural diagram of a network architecture for video encoding / decoding according to an embodiment of the present application. As shown in FIG. 2, the network architecture includes one or more electronic devices 11 to 1N and a communication network 01, where the electronic devices 11 to 1N can perform video interaction via the communication network 01. In the implementation process, the electronic devices can be various types of devices with video encoding / decoding functions. For example, the electronic devices can include mobile phones, tablet computers, personal computers, personal digital assistants, navigators, digital telephones, video telephones, televisions, sensing devices, servers, etc., but the embodiment of the present application is not limited thereto. Here, the intra-frame prediction device in the embodiment of the present application can be any of the above electronic devices.

[0025] Here, the electronic device in the embodiment of the present application has a video encoding / decoding function, and typically includes a video decoder and a video decoder.

[0026] 3A, the configuration of the video encoder 21 includes a transform and quantization unit 211, an intraframe estimation unit 212, an intraframe prediction unit 213, a motion compensation unit 214, a motion estimation unit 215, an inverse transform and inverse quantization unit 216, a filter control analysis unit 217, a filtering unit 218, an entropy encoding unit 219, and a decoded image cache unit 210. Here, the filtering unit 218 may implement deblocking filtering and sample adaptive offset (SAO) filtering, and the entropy encoding unit 219 may implement header information coding and context-based adaptive binary arithmetic coding (CABAC). For the input source video data, a block to be coded of a current video frame can be obtained by dividing a coding tree unit (CTU). Then, residual information obtained by performing intra-frame prediction or inter-frame prediction on the block to be coded is transformed by the transform and quantization unit 211 (including converting the residual information from the pixel domain to the transform domain and quantizing the resulting transform coefficients, thereby further reducing the bit rate). The intra-frame estimation unit 212 and the intra-frame prediction unit 213 are configured to perform intra-frame prediction on the block to be coded, for example, to determine an intra-frame prediction mode to be used for coding the block to be coded. The motion compensation unit 214 and the motion estimation unit 215 are configured to perform inter-frame predictive coding of the block to be coded with respect to one or more blocks in one or more reference frames, thereby providing temporal prediction information. Here, the motion estimation unit 215 is configured to estimate a motion vector, and the motion of the block to be coded can be estimated based on the motion vector. Then, the motion compensation unit 214 performs motion compensation based on the motion vector. After determining the intra prediction mode, the intra prediction unit 213 is further configured to provide the selected intra 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. Furthermore, the inverse transform and inverse quantization unit 216 is configured to reconstruct the block to be coded, i.e., reconstruct a residual block in the pixel domain, and artifacts caused by blocking effects in the reconstructed residual block are removed by the filter control analysis unit 217 and the filtering unit 218, and then add the reconstructed residual block to one prediction block in the frame of the decoded image cache unit 210 to generate a reconstructed video coding block.The entropy coding unit 219 is configured to code various coding parameters and quantized transform coefficients; in a CABAC-based coding algorithm, the context content may be based on neighboring coding blocks, and information indicating a determined intra-frame prediction mode may be coded and used to output a bitstream of the video data. The decoded picture cache unit 210 is configured to store reconstructed video coding blocks used for prediction reference. As the video is coded, new reconstructed video coding blocks are continuously generated, and these reconstructed video coding blocks are stored in the decoded picture cache unit 210.

[0027] As shown in FIG. 3B, the video decoder 22 corresponding to the video encoder 21 includes an entropy decoding unit 221, an inverse transform and inverse quantization unit 222, an intraframe prediction unit 223, a motion compensation unit 224, a filtering unit 225, and a decoded image cache unit 226. The entropy decoding unit 221 performs header information decoding and CABAC decoding, and the filtering unit 225 performs deblocking filtering and SAO filtering. After performing the encoding process shown in FIG. 3A on the input video signal, the decoder outputs a bitstream of the video signal. The bitstream is input to the video decoder 22 and first processed by the entropy decoding unit 221 to obtain decoded transform coefficients. The transform coefficients are then processed by the inverse transform and inverse quantization unit 222 to generate residual blocks in the pixel domain. The intra prediction unit 223 may be configured to generate prediction data for the currently decoded block based on the determined intra prediction mode and data of a previously decoded block from the current frame or image. The motion compensation unit 224 analyzes the motion vectors and other relevant syntax elements to determine prediction information for the currently decoded block and uses the prediction information to generate a prediction block for the currently decoded block being decoded. The residual block from the inverse transform and inverse quantization unit 222 is summed with the corresponding prediction block generated by the intra prediction unit 223 or the motion compensation unit 224 to generate a decoded video block. Artifacts due to blocking effects in the decoded video block are removed by the filtering unit 225, thereby improving quality. The decoded video block is then stored in the decoded image cache unit 226, which stores reference images used for subsequent intra prediction or motion compensation, and for output display of the video signal.

[0028] Based on this, the technical solution of the present application will be described in more detail below with reference to the accompanying drawings and embodiments. The video image component prediction method provided in the embodiments of the present application refers to prediction in an intra-frame prediction process for predictive encoding and decoding, that is, it may be provided to a video encoder 21 or applied to a video decoder 22, but the embodiments of the present application are not limited thereto.

[0029] In the next-generation video coding standard H.266, cross-component prediction (CCP) has been extended and improved, and cross-component linear model prediction (CCLM) has been proposed to further improve coding / decoding performance and efficiency. In H.266, CCLM realizes prediction from the luma component to the blue-difference component, from the luma component to the red-difference component, and between the blue-difference component and the red-difference component. Below, we will explain the video component prediction method based on the conventional CCLM.

[0030] An embodiment of the present application provides a video image component prediction method, which is applied to a video image component prediction device, and functions realized by the method can be realized by calling a program code by a processor in the video image component prediction device. Of course, the program code can be stored in a computer storage medium, and obviously, the video image component prediction device includes at least a processor and a storage medium.

[0031] FIG. 4 is a flowchart illustrating the implementation of a video image component prediction method in an embodiment of the present application. As shown in FIG. 4, the method includes the following steps:

[0032] In step S101, the first image component reference value of the current block is obtained.

[0033] In step S102, a plurality of first image component reference values ​​are determined from the set of reference values ​​for the first image component.

[0034] In step S103, a first filtering process is performed on the sample values ​​of pixel points corresponding to the plurality of first image component reference values, respectively, to obtain a plurality of filtered first image reference sample values.

[0035] In step S104, a prediction-to-be-predicted image component reference value corresponding to the plurality of filtered first image reference sample values ​​is determined, where the prediction-to-be-predicted image component is an image component different from the first image component.

[0036] In step S105, parameters of a component linear model are determined based on the plurality of filtered first image reference sample values ​​and the prediction-waiting image component reference values, where the component linear model represents a linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the prediction-waiting image component.

[0037] In step S106, a mapping process is performed on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapping value.

[0038] In step S107, a predicted value of the image component of the current block to be predicted is determined based on the mapping value.

[0039] In step S101, in an embodiment of the present application, the current block is a coding block or a decoding block waiting for image component prediction. In this embodiment, the video image component prediction device obtains a first image component reference value of the current block, where the first image component reference value set includes one or more first image component reference values. The reference value of the current block can be obtained from a reference block, which can be a neighboring block of the current block or a non-neighboring block of the current block, but this embodiment is not limited thereto.

[0040] In some embodiments of the present application, the video image component prediction device determines one or more reference pixel points located at positions other than the current block, and determines the one or more reference pixel points as one or more first image component reference values.

[0041] In the embodiments of the present application, the adjacent processing block corresponding to the current block is a processing block adjacent to one or more sides of the current block, and the one or more adjacent sides may refer to the top side adjacent to the current block, or may refer to the left side adjacent to the current block, or may refer to the top side and left side adjacent to the current block, but it should be noted that the embodiments of the present application are not limited thereto.

[0042] In some embodiments of the present application, the video image component prediction device determines pixel points adjacent to the current block as one or more reference pixel points.

[0043] In the embodiment of the present application, the one or more reference pixel points may be adjacent pixel points or non-adjacent pixel points, but it should be noted that the embodiment of the present application is not limited thereto. In the present application, adjacent pixel points are described as an example.

[0044] Here, pixel points adjacent to one or more sides of the current block corresponding to adjacent processing blocks are used as one or more adjacent reference pixel points corresponding to the current block, and each adjacent reference pixel point 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 at each adjacent reference pixel point of the one or more adjacent reference pixel points corresponding to the current block as a first image component reference value set, thereby obtaining one or more first image component reference values. That is, the one or more first image component reference values ​​represent the reference values ​​of the corresponding first image components of one or more adjacent pixel points in the adjacent reference blocks corresponding to the current block. Here, the first image components in the embodiments of the present application are used to predict other image components.

[0045] In some embodiments of the present application, the combination of the first image component and the prediction-to-be-predicted image component includes at least one of the following:

[0046] The first image component is a luminance component, and the image component to be predicted is a first or second chrominance component, or The first image component is a first chrominance component, and the image component to be predicted is a luminance component or a second chrominance component, or The first image component is a second chrominance component, and the image component to be predicted is a luminance component or a first chrominance component, or 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 The first image component is the second color component, and the image component to be predicted is the first color component or the third color component, or The first image component is the third color component, and the image component to be predicted is the second color component or the first color component.

[0047] In some embodiments of the present 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.

[0048] Here, the first color difference component may be a blue color difference component and the second color difference component may be a red color difference component, or the first color difference component may be a red color difference component and the second color difference component may be a blue color difference component, where the first color difference component and the second color difference component may simply represent the blue color difference component and the red color difference component, respectively.

[0049] An example will be described in which the first color difference component may be a blue color difference component and the second color difference component may be a red color difference component. When the first image component is a luma component and the image component to be predicted is the first color difference component, the video image component prediction device can predict the blue color difference component using the luma component. When the first image component is a luma component and the image component to be predicted is the second color difference component, the video image component prediction device can predict the red color difference component using the luma component. When the first image component is a first color difference component and the image component to be predicted is the second color difference component, the video image component prediction device can predict the red color difference component using the blue color difference component. When the first image component is a second color difference component and the image component to be predicted is the first color difference component, the video image component prediction device can predict the blue color difference component using the red color difference component.

[0050] In step S102, the video image component prediction device can determine a plurality of first image component reference values ​​from one or more first image component reference values.

[0051] In some embodiments of the present application, the video image component prediction device may compare one or more first image component reference values ​​included in a first image component reference value set to determine a maximum first image component reference value and a minimum first image component reference value.

[0052] In some embodiments of the present application, the video image component prediction device can determine the maximum and minimum values ​​of a plurality of first image component reference values ​​from one or more first image component reference values, and can determine a reference value representing the maximum or minimum first image component reference value from the one or more first image component reference values.

[0053] For example, the video image component prediction device determines a maximum first image component reference value and a minimum first image component reference value from a set of first image component reference values.

[0054] In the embodiment of the present application, the video image component prediction device can obtain the maximum first image component reference value and the minimum first image component reference value in various ways.

[0055] In Method 1, each first image component reference value of one or more first image component reference values ​​is compared in turn to determine the largest single first image component reference value and the smallest single first image component reference value.

[0056] In method 2, at least two first image component reference values ​​at preset positions are selected from one or more first image component reference values, and the at least two first sub-image component reference values ​​are divided into a maximum image component reference value set and a minimum image component reference value set according to the magnitude of their numerical values, and the maximum first image component reference value and the minimum first image component reference value are obtained based on the maximum image component reference value set and the minimum image component reference value set.

[0057] That is, in the embodiment of the present application, the video image component prediction device selects the first image component reference value with the largest value as the largest first image component reference value from one or more first image component reference values, and selects the first image component reference value with the smallest value as the smallest first image component reference value. The determination method may be a sequential comparison of two values ​​at a time, or may be determined after sorting, but the embodiment of the present application does not limit the specific determination method.

[0058] The video image component prediction device may also select several first image component reference values ​​corresponding to preset positions (preset pixel point positions) from pixel point positions corresponding to one or more first image component reference values ​​as at least two first image component reference values, and then divide based on the at least two first image component reference values ​​to obtain a maximum data set (maximum image component reference value set) and a minimum data set (minimum image component reference value set), and determine the maximum first image component reference value and the minimum first image component reference value based on the maximum data set and the minimum data set. Here, the process of determining the maximum first image component reference value and the minimum first image component reference value based on the maximum data set and the minimum data set may be to perform an averaging process on the maximum data set to obtain the maximum first image component reference value, and perform an averaging process on the minimum data set to obtain the minimum first image component reference value. The maximum and minimum values ​​may also be determined using other methods, but the embodiment of the present application is not limited thereto.

[0059] The number of values ​​in the largest and smallest data sets are integers greater than or equal to 1, and the number of values ​​in the two sets may be the same or different, although it should be noted that embodiments of the present application are not limited thereto.

[0060] After determining several first image component reference values ​​corresponding to preset positions as at least two first sub-image component reference values, the video image component prediction device can also directly select the maximum value among the at least two first sub-image component reference values ​​as the maximum first image component reference value, and directly select the minimum value among the at least two first sub-image component reference values ​​as the minimum first image component reference value.

[0061] Illustratively, the video image component prediction device may select M (M may be a value greater than 4 or may not be limited) largest first sub-image component reference values ​​among the at least two first sub-image component reference values ​​as a maximum image component reference value set, select at least two first sub-image component reference values ​​other than the M largest first sub-image component reference values ​​as a minimum image component reference value set, and finally, perform an averaging process on the maximum image component reference value set to obtain the maximum first image component reference value, and perform an averaging process on the minimum image component reference value set to obtain the minimum first image component reference value.

[0062] In this embodiment, the maximum and minimum first image component reference values ​​may be directly determined by the magnitude of the numerical values. Alternatively, first image component reference values ​​(at least two first sub-image component reference values) that can represent the validity of the reference values ​​at a preset position are selected, and then the valid first image component reference values ​​are divided into one set of relatively large numerical values ​​and one set of relatively small numerical values. Then, the maximum and minimum first image component reference values ​​are determined based on the set of relatively large numerical values, and the minimum first image component reference values ​​are determined based on the set of relatively small numerical values. Alternatively, the maximum and minimum first image component reference values ​​are directly determined from the set of valid first image component reference values ​​corresponding to the preset position according to the magnitude of the numerical values.

[0063] In the embodiment of the present application, the video image component prediction device does not limit the manner in which it determines the maximum and minimum first image component reference values. For example, the video image component prediction device may divide one or more first image component reference values ​​into three or four sets according to their sizes, process each set to obtain one representative parameter, and then select the maximum and minimum parameters from the representative parameters as the maximum and minimum first image component reference values.

[0064] In the embodiment of the present application, the selection of the preset positions can be a position that represents the validity of the first image component reference value, and the number of the preset positions is not limited (for example, it can be 4 or 6). The preset positions can be all positions of the neighboring pixel points, but the embodiment of the present application is not limited thereto.

[0065] For example, a preset number of first image component reference values ​​may be selected from both sides of the center of the row or column at a preset position according to the sampling frequency, or the first image component reference values ​​may be located at other positions excluding the edge positions of the row or column, but the embodiments of the present application are not limited thereto.

[0066] The preset positions in the rows and columns may be allocated uniformly or in a preset manner, but the embodiments of the present application are not limited thereto. For example, if the number of preset positions is four and adjacent rows and columns correspond to one or more first image component reference values, two first image component reference values ​​may be selected from the first image component reference values ​​corresponding to the adjacent rows, and two first image component reference values ​​may be selected from the first image component reference values ​​corresponding to the adjacent columns. Alternatively, one first image component reference value may be selected from the first image component reference values ​​corresponding to the adjacent rows, and three first image component reference values ​​may be selected from the first image component reference values ​​corresponding to the adjacent columns, but the embodiments of the present application are not limited thereto.

[0067] The video image component prediction device can determine the maximum and minimum values ​​of the one or more first image component reference values ​​from one or more first image component reference values. That is, the maximum value (maximum first image component reference value) of the one or more first image component reference values ​​and the minimum value (minimum first image component reference value) of the one or more first image component reference values ​​are obtained. Alternatively, after determining multiple reference values ​​from preset positions of one or more first image component reference values, the maximum and minimum first image component reference values ​​are obtained through processing. Here, in order to match or approach the sampling positions of other video components, filtering needs to be performed based on the pixel point positions corresponding to the maximum and minimum first image component reference values ​​before subsequent processing.

[0068] In step S103, the video image component prediction device performs a first filtering process on the sample values ​​of the pixel points corresponding to the determined plurality of first image component reference values, respectively, to obtain a plurality of filtered first image reference sample values.

[0069] In an embodiment of the present application, the plurality of filtered first image reference sample values ​​may be the maximum filtered first image component reference value and the minimum filtered first image component reference value, or may be a plurality of reference sample values ​​including the maximum filtered first image component reference value and the minimum filtered first image component reference value, or may be other plurality of reference sample values, but the embodiment of the present application is not limited thereto.

[0070] In an embodiment of the present application, the video image component prediction device performs a filtering process (i.e., a first filtering process) on pixel point positions (i.e., sample values ​​of the corresponding pixel points) corresponding to the determined first image component reference values, thereby obtaining corresponding multiple filtered first image reference sample values, and thereby constructing a component linear model based on the multiple filtered first image reference sample values.

[0071] In some embodiments of the present application, the video image component prediction device performs a first filtering process on sample values ​​of pixel points corresponding to the maximum first image component reference value and the minimum first image component reference value, respectively, to obtain the maximum first image component reference value after filtering and the minimum first image component reference value after filtering.

[0072] It should be noted that since the determined plurality of first image component reference values ​​may be the maximum first image component reference value and the minimum first image component reference value, the filtering process may perform a filtering process (i.e., a first filtering process) on the pixel point positions (i.e., the sample values ​​of the corresponding pixel points) to determine the maximum first image component reference value and the minimum first image component reference value, thereby obtaining the corresponding maximum filtered first image component reference value and minimum filtered image component reference value (i.e., a plurality of filtered first image reference sample values), and in this way, a component linear model may be subsequently constructed based on the maximum filtered first image component reference value and the minimum filtered first image component reference value.

[0073] In the present embodiment, the filtering method may be, but is not limited to, upsampling, downsampling, low-pass filtering, etc. Here, the downsampling method may include, but is not limited to, averaging, interpolation, median, etc.

[0074] In the present embodiment, the first filtering process may be downsampling filtering and low-pass filtering.

[0075] Illustratively, the video image component prediction device may perform downsampling filtering on pixel point positions for determining the maximum first image component reference value and the minimum first image component reference value to obtain corresponding filtered maximum first image component reference value and filtered minimum first image component reference value.

[0076] Hereinafter, an example will be described in which downsampling is an average method.

[0077] The video component prediction device performs an average calculation of the first image component for a region formed by the position corresponding to the maximum first image component reference value and its adjacent pixel point positions, fuses the pixels of this block region into one pixel, and the average result is the first image component reference value corresponding to the pixel point after the fusion, i.e., the maximum first image component reference value after filtering. Similarly, the video component prediction device performs an average calculation of the first image component for a region formed by the position corresponding to the minimum first image component reference value and its adjacent pixel point positions, fuses the pixels of this block region into one pixel, and the average result is the first image component reference value corresponding to the pixel point after the fusion, i.e., the minimum first image component reference value after filtering.

[0078] In addition, in the embodiment of the present application, the downsampling process of the video image component prediction device is realized by a filter, and specifically, the position range of the vector pixel points adjacent to the position corresponding to the maximum first image component reference value can be determined by the type of filter, but the embodiment of the present application is not limited to this.

[0079] In the embodiment of the present application, the type of the filter may be a 6-tap filter or a 4-tap filter, but the embodiment of the present application is not limited thereto.

[0080] In steps S104 and S105, the video image component prediction device determines image component reference values ​​to be predicted corresponding to the plurality of filtered first image reference sample values, where the image component to be predicted is an image component different from the first image component (e.g., the second image component or the third image component), and then determines parameters of a component linear model based on the plurality of filtered first image reference sample values ​​and the image component reference values ​​to be predicted, where the component linear model represents a linear mapping relationship (functional relationship) that maps the sample values ​​of the first image component to the sample values ​​of the image component to be predicted.

[0081] In some embodiments of the present application, the video image component prediction device determines a maximum prediction-to-be-predicted image component reference value corresponding to the maximum first image component reference value after filtering, and a minimum prediction-to-be-predicted image component reference value corresponding to the minimum first image component reference value after filtering.

[0082] In addition, in the embodiment of the present application, the video image component prediction device can construct a component linear model (i.e., a simplified cross-component linear model prediction (CCLM)) by deriving model parameters (i.e., parameters of the component linear model) according to the principle of "determining a line by two points" using a maximum and minimum value construction method.

[0083] In an embodiment of the present application, the video image component prediction device performs downsampling (i.e., filtering) to achieve alignment with the position of the image to be predicted. In this way, it is possible to determine the image component reference value to be predicted corresponding to the first image component reference sample value after filtering. For example, it 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. In this way, the video image component prediction device determines two points, namely (the maximum first image component reference value after filtering, the maximum image component reference value to be predicted) and (the minimum first image component reference value after filtering, the minimum image component reference value to be predicted), and therefore, it is possible to derive model parameters and construct a component linear model according to the principle of "determining a line with two points."

[0084] In some embodiments of the present application, the video image component prediction device determines parameters of a component linear model based on the maximum first image component reference value after filtering, the maximum image component reference value to be predicted, the minimum first image component reference value after filtering, and the minimum image component reference value to be predicted, where the component linear model represents a linear mapping relationship that maps sample values ​​of the first image component to sample values ​​of the image component to be predicted.

[0085] In some embodiments of the present application, a video image component prediction device may determine parameters of a component linear model based on the maximum filtered first image component reference value, the maximum predicted image component reference value, the minimum filtered first image component reference value, and the minimum predicted image component reference value, including the following: In method (1), the parameters of the component linear model include a multiplicative factor and an additive offset. Therefore, the video image component prediction device may calculate a first difference value between the maximum predicted image component reference value and the minimum predicted image component reference value, calculate a second difference value between the maximum predicted image component reference value and the minimum first image component reference value, set the multiplicative factor as a ratio of the first difference value and the second difference value, calculate a first product between the maximum predicted image component reference value and the multiplicative factor, and set the additive offset as the difference value between the maximum predicted image component reference value and the first product; or calculate a second product between the minimum predicted image component reference value and the multiplicative factor, and set the additive offset as the difference value between the minimum predicted image component reference value and the second product. In method (2), a first sub-component linear model is constructed using the maximum first image component reference value after filtering, the maximum image component reference value waiting for prediction, and a preset initial linear model; a second sub-component linear model is constructed using the minimum first image component reference value after filtering, the minimum image component reference value waiting for prediction, and a preset initial linear model; model parameters are obtained based on the first sub-component linear model and the second sub-component linear model; and a component linear model is constructed using the model parameters and the preset initial linear model.

[0086] Here, the above values ​​are determined or designed according to actual conditions, and the embodiments of the present application are not limited thereto.

[0087] Illustratively, since the component linear model represents a 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, and the image component to be predicted in the embodiment of the present application can be a chrominance component.

[0088] Exemplarily, the component linear model may be as shown in equation (1) below.

[0089] C=αY+β (1) where Y represents the first image component reconstruction value corresponding to a particular pixel point in the current block (downsampled), C represents the second image component prediction value corresponding to the particular pixel point in the current block, and α and β are model parameters of the component linear model.

[0090] Here, the specific realization of the model parameters will be explained in detail in the following examples.

[0091] The video image component prediction device selects the maximum and minimum first image component reference values ​​based on one or more directly obtained first image component reference values ​​corresponding to the current block, and then performs downsampling based on the positions corresponding to the selected maximum and minimum first image component reference values ​​to construct a component linear model. In this way, the workload of the downsampling process for pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

[0092] In steps S106 and S107, in an embodiment of the present application, after obtaining the component linear model, the video image component prediction device can directly perform video component prediction on the current block using the component linear model, thereby obtaining a predicted value of the image component to be predicted. Here, the video image component prediction device can perform a mapping process on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapping value, and then determine a predicted value of the image component to be predicted of the current block based on the mapping value.

[0093] In some embodiments of the present application, the video image component prediction device performs a second filtering operation on the reconstructed value of the first image component to obtain a second filter value of the reconstructed value of the first image component, and performs a mapping operation on the second filter value based on the component linear model to obtain a mapping value.

[0094] In some embodiments of the present application, the video image component predictor sets the mapping value as the predicted value of the image component of the current block to be predicted.

[0095] Here, the second filtering process may be downsampling filtering or low-pass filtering.

[0096] In some embodiments of the present application, the video image component prediction device can also perform a third filtering process on the mapping value to obtain a third filter value of the mapping value, and set the third filter value as the predicted value of the image component to be predicted of the current block.

[0097] Here, the third filtering process may be low-pass filtering.

[0098] In the present embodiment, the predicted value represents a predicted value of the second image component or a predicted value of the third image component corresponding to one or more pixel points of the current block.

[0099] In the process of constructing the component linear model, a plurality of first image component reference values ​​are selected, and then a filtering process is performed based on the positions corresponding to the selected plurality of first image component reference values ​​to construct the component linear model. This reduces the workload of the filtering process for pixel points corresponding to the current block, i.e., reduces the filtering operation, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

[0100] In some embodiments of the present application, as shown in FIG. 5, the embodiments of the present application further provide a video image component prediction method, the method including the following steps:

[0101] In step S201, a set of reference values ​​for the first image component of the current block is obtained.

[0102] In step S202, the reference values ​​in the set of reference values ​​for the first image component are compared to determine the maximum and minimum first image component reference values.

[0103] In step S203, a first filtering process is performed on the sample values ​​of the pixel points corresponding to the maximum first image component reference value and the minimum first image component reference value, respectively, to obtain the maximum first image component reference value after filtering and the minimum first image component reference value after filtering.

[0104] In step S204, the maximum prediction-awaiting image component reference value corresponding to the maximum first image component reference value after filtering and the minimum prediction-awaiting image component reference value corresponding to the minimum first image component reference value after filtering are determined.

[0105] In step S205, parameters of a component linear model are determined based on the maximum first image component reference value after filtering, the maximum image component reference value awaiting prediction, the minimum first image component reference value after filtering, and the minimum image component reference value awaiting prediction, where the component linear model represents a linear mapping relationship that maps sample values ​​of the first image component to sample values ​​of the image component awaiting prediction.

[0106] In step S206, a mapping process is performed on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapping value.

[0107] In step S207, a predicted value of the image component of the current block to be predicted is determined based on the mapping value.

[0108] In the embodiment of the present application, the process of steps S201 to S207 has already been described in the above embodiment, and will not be described again here.

[0109] In addition, when the video image component prediction device performs prediction, for the first image component reconstruction value of the current block, first image component filtering is performed on the current block to obtain the first image component reconstruction value corresponding to the current block, and then a predicted value of the image component to be predicted of the current block is obtained based on the component linear model and the first image component reconstruction value.

[0110] In the embodiment of the present application, after the video image component prediction device obtains the component linear model, since the smallest unit for predicting the current block is a pixel point, it is necessary to predict the predicted value of the image component to be predicted corresponding to each pixel point of the current block using the first image component reconstruction value corresponding to the pixel point. Here, the video image component prediction device 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, obtains the first image component reconstruction value of each pixel point corresponding to the current block.

[0111] In the present embodiment, the first image component reconstruction values ​​represent reconstruction values ​​of the first image component corresponding to one or more pixel points of the current block.

[0112] As a result, the video image component prediction device can perform a mapping process on the reconstructed value of the first image component of the current block using the component linear model to obtain a mapping value, and based on the mapping value, obtain a predicted value of the image component of the current block to be predicted.

[0113] In some embodiments of the present application, as shown in FIG. 6, a specific implementation of step S204 may include steps S2041 to S2042 as follows:

[0114] In step S2041, the prediction-waiting image component reference value of the current block is obtained.

[0115] In step S2042, the maximum and minimum prediction-waiting image component reference values ​​are determined from the prediction-waiting image component reference values.

[0116] In an embodiment of the present application, the video image component prediction device constructs a component linear model based on the maximum image component reference value after filtering and the minimum image component reference value after filtering. In this process, based on the principle of "determining a line by two points", when the first image component is the abscissa and the image component to be predicted is the ordinate, the values ​​of the abscissa of the two points are known, and before determining a linear model, i.e., a component linear model, based on the principle of "determining a line by two points", the values ​​of the ordinate corresponding to the two points need to be determined.

[0117] In some embodiments of the present application, the video image component prediction device converts the sampling point (Sample) position of the first image component reference value corresponding to the maximum first image component reference value to the position of the first sampling point, sets the maximum prediction-awaiting image component reference value as a reference value at the position of the first sampling point among the prediction-awaiting image component reference values, converts the sampling point position of the first image component reference value corresponding to the minimum first image component reference value to the position of the second sampling point, and sets the minimum prediction-awaiting image component reference value as a reference value at the position of the second sampling point among the prediction-awaiting image component reference values.

[0118] For illustrative purposes, the reference pixel point is taken as an example to be an adjacent pixel point. Based on the above description of the adjacent block, the video image component prediction device can obtain one or more prediction-waiting image component reference values ​​corresponding to the current block, where the one or more prediction-waiting image component reference values ​​may refer to the reference values ​​of the prediction-waiting image components at the adjacent reference pixel points of the one or more reference pixel points corresponding to the current block, and use it as one prediction-waiting image component reference value. In this way, the video image component prediction device obtains one or more prediction-waiting image component reference values.

[0119] The video image component prediction device finds a first adjacent reference pixel point corresponding to the largest first image component reference value after filtering from pixel points corresponding to one or more image component reference values ​​to be predicted, and uses the image component reference value corresponding to the first adjacent reference pixel point as the largest image component reference value to be predicted after filtering, i.e., determines the largest image component reference value to be predicted corresponding to the largest first image component reference value after filtering; finds a second adjacent reference pixel point corresponding to the one or more image component reference values ​​to be predicted, and uses the image component reference value corresponding to the second adjacent reference pixel point as the smallest image component reference value to be predicted after filtering, i.e., determines the smallest image component reference value to be predicted corresponding to the smallest first image component reference value after filtering. Finally, according to the principle of "determining a line by two points," a straight line is determined based on two points: (the largest first image component reference value after filtering, the largest image component reference value to be predicted) and (the smallest first image component reference value after filtering, the smallest image component reference value to be predicted), and the function (mapping relationship) represented by the straight line is a component linear model.

[0120] In some embodiments of the present application, the video image component prediction device also first performs filtering on adjacent pixel point positions to obtain one or more prediction-awaiting image component reference values ​​of the pixel point after filtering, then finds a first adjacent reference pixel point from the filtered pixel point position where the maximum first image component reference value after filtering is located, and uses the prediction-awaiting image component reference value (one of the one or more prediction-awaiting image component reference values) corresponding to the first adjacent reference pixel point as the maximum prediction-awaiting image component reference value, that is, determines the maximum prediction-awaiting image component reference value corresponding to the maximum first image component reference value after filtering, finds a second adjacent reference pixel point from the filtered pixel point position where the minimum first image component reference value after filtering is located, and uses the prediction-awaiting image component reference value corresponding to the second adjacent reference pixel point as the minimum prediction-awaiting image component reference value, that is, determines the minimum prediction-awaiting image component reference value corresponding to the minimum first image component reference value after filtering.

[0121] It should be noted that the video image component prediction device can also first perform filtering on neighboring pixel point positions, and such a process is filtering the image component to be predicted (e.g., chrominance image component), and the embodiment of the present application is not limited thereto. That is, in the embodiment of the present application, the video image component prediction device can perform a fourth filtering process on the image component reference value to be predicted to obtain the image component reconstruction value to be predicted.

[0122] Here, the fourth filtering process may be low-pass filtering.

[0123] In some embodiments of the present application, the process of the video image component prediction device constructing a component linear model includes: constructing a first sub-component linear model using the maximum first image component reference value after filtering, the maximum image component reference value to be predicted, and a preset initial linear model; constructing a second 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; obtaining model parameters based on the first sub-component linear model and the second sub-component linear model; and constructing a component linear model using the model parameters and the preset initial linear model.

[0124] In the present embodiment, the preset initial linear model is an initial model whose model parameters are unknown.

[0125] For example, the preset initial linear model may be in the form of Equation (1), in which α and β are unknown. A second equation with two variables can be constructed using the first sub-component linear model and the second sub-component linear model to determine the model parameters α and β. By substituting α and β into Equation (1), a linear mapping relationship model between the first image component and the image component to be predicted can be obtained.

[0126] For example, the model parameters (α and β shown in the following equation (2)) are derived according to the principle of "determining a line by two points" by finding the maximum first image component reference value (the maximum first image component reference value after filtering) and the minimum first image component reference value (the minimum first image component reference value after filtering).

number

[0127] where L max and L min represents the maximum and minimum values ​​found from the first image component reference values ​​corresponding to the left and / or top edges that are not downsampled, and C max and C min L max and L min 7 is a schematic structural diagram showing how to construct a prediction model based on the maximum and minimum values ​​of the current block. Here, the horizontal coordinate represents the first image component reference value of the current block, the vertical coordinate represents the image component reference value of the current block to be predicted, and L max and L min , and C max and C min According to the above, the model parameters α and β can be calculated through equation (2), and the constructed prediction model is C=αY+β. In the actual prediction process, Y represents the first image component reconstruction value corresponding to a pixel point in the current block, and C represents the image component prediction value to be predicted corresponding to the pixel point in the current block.

[0128] Apparently, the video image component prediction device first selects the maximum and minimum first image component reference values ​​based on one or more directly obtained first image component reference values ​​corresponding to the current block, and then performs downsampling (filtering) based on the positions corresponding to the selected maximum and minimum first image component reference values ​​to construct a component linear model. In this way, the workload of the downsampling process for pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

[0129] Based on the above embodiments, the embodiments of the present application provide a video content prediction device, and each unit and each module included in each unit of the device can be realized by a processor in the video content prediction device, and of course can also be realized by a specific logic circuit. 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.

[0130] As shown in FIG. 8, the video component prediction device 3 according to the embodiment of the present application includes: an acquisition unit 30 configured to acquire a set of reference values ​​for a first image component of a current block, said set of reference values ​​for the first image component comprising one or more first image component reference values; a determiner 31 configured to determine a plurality of first image component reference values ​​from the set of reference values ​​for the first image component; a filtering unit (32) configured to perform a first filtering process on the sample values ​​of pixel points corresponding to the plurality of first image component reference values, respectively, to obtain a plurality of filtered first image reference sample values; a prediction unit 33 configured to determine a prediction value of the prediction-to-be-predicted image component of the current block based on the mapping value; The determination unit 31 is further configured to determine image component reference values ​​to be predicted corresponding to the plurality of filtered first image reference sample values, the image component to be predicted being an image component different from the first image component, and determine parameters of a component linear model based on the plurality of filtered first image reference sample values ​​and the image component reference values ​​to be predicted, the component linear model representing a linear mapping relationship that maps sample values ​​of the first image component to sample values ​​of the image component to be predicted, The filtering unit 32 is further configured to perform a mapping operation on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapped value.

[0131] In some embodiments of the present application, the determination unit 31 is further configured to compare the reference values ​​included in the reference value set for the first image component to determine the maximum first image component reference value and the minimum first image component reference value.

[0132] In some embodiments of the present application, the filtering unit 32 is further configured to perform the first filtering process on sample values ​​of pixel points corresponding to the maximum first image component reference value and the minimum first image component reference value, respectively, to obtain a filtered maximum first image component reference value and a filtered minimum first image component reference value.

[0133] In some embodiments of the present application, the determination unit 31 is further configured to determine a maximum prediction-waiting image component reference value corresponding to the maximum first image component reference value after the filtering, and a minimum prediction-waiting image component reference value corresponding to the minimum first image component reference value after the filtering.

[0134] In some embodiments of the present application, the determination unit 31 is further configured to determine parameters of a component linear model based on the maximum first image component reference value after filtering, the maximum image component reference value to be predicted, the minimum first image component reference value after filtering, and the minimum image component reference value to be predicted, and the component linear model represents a linear mapping relationship that maps sample values ​​of the first image component to sample values ​​of the image component to be predicted.

[0135] In some embodiments of the present application, the determining unit 31 is further configured to determine one or more reference pixel points located outside the current block; The obtaining unit 30 is further configured to determine the one or more reference pixel points as the one or more first image component reference values.

[0136] In some embodiments of the present application, the determining unit 31 is further configured to determine pixel points adjacent to the current block as the one or more reference pixel points.

[0137] In some embodiments of the present application, the filtering unit 32 is further configured to perform a second filtering operation on the reconstructed values ​​of the first image component to obtain second filter values ​​of the reconstructed values ​​of the first image component, and to perform a mapping operation on the second filter values ​​based on the component linear model to obtain the mapped values.

[0138] In some embodiments of the present application, the second filtering process is downsampling filtering or low-pass filtering.

[0139] In some embodiments of the present application, the prediction unit 33 is further configured to set the mapping value as a predicted value of the prediction-to-be-predicted image component of the current block.

[0140] In some embodiments of the present application, the filtering unit 32 is further configured to perform a third filtering operation on the mapped values ​​to obtain third filtered values ​​of the mapped values; The prediction unit 33 is further configured to set the third filter value as a predicted value of the prediction-awaiting image component of the current block.

[0141] In some embodiments of the present application, the third filtering process is low-pass filtering.

[0142] In some embodiments of the present application, the determination unit 31 is further configured to obtain prediction-waiting image component reference values ​​of the current block, and determine the maximum prediction-waiting image component reference value and the minimum prediction-waiting image component reference value from the prediction-waiting image component reference values.

[0143] In some embodiments of the present application, the filtering unit 32 is further configured to perform a fourth filtering operation on the prediction-to-be-predicted image component reference value to obtain a prediction-to-be-predicted image component reconstruction value.

[0144] In some embodiments of the present application, the fourth filtering process is low-pass filtering.

[0145] In some embodiments of the present application, the determination unit 31 is further configured to convert the position of the sampling point of the first image component reference value corresponding to the maximum first image component reference value to the position of a first sampling point, set the maximum image component reference value to be predicted as the reference value at the position of the first sampling point among the image component reference values ​​to be predicted, convert the position of the sampling point of the first image component reference value corresponding to the minimum first image component reference value to the position of a second sampling point, and set the minimum image component reference value to be predicted as the reference value at the position of the second sampling point among the image component reference values ​​to be predicted.

[0146] In some embodiments of the present application, the determination unit 31 is further configured to construct a first sub-component linear model using the maximum first image component reference value after filtering, the maximum image component reference value to be predicted, and a preset initial linear model, construct a second 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, 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.

[0147] In some embodiments of the present application, the determiner 31 is further configured such that parameters of the component linear model include a multiplicative factor and an additive offset; calculate a first difference value between the maximum image component reference value to be predicted and the minimum image component reference value to be predicted; calculate a second difference value between the maximum first image component reference value and the minimum first image component reference value; set the multiplicative factor as a ratio of the first difference value to the second difference value; calculate a first product between the maximum first image component reference value and the multiplicative factor; and set the additive offset as the difference value 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 value between the minimum image component reference value to be predicted and the second product.

[0148] In some embodiments of the present application, the first image component is a luminance component, and the image component to be predicted is a first or second chrominance component, or the first image component is the first color difference component, and the image component to be predicted is the luminance component or the second color difference component, or the first image component is the second chrominance component, and the image component to be predicted is the luminance component or the first chrominance component, or 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 the first image component is the second color component, and the image component to be predicted is the first color component or the third color component, or The first image component is the third color component, and the image component to be predicted is the second color component or the first color component.

[0149] In some embodiments of the present 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.

[0150] In some embodiments of the present application, the first filtering process is downsampling filtering or low-pass filtering.

[0151] In addition, in the embodiments of the present application, the above-described video component prediction method may be implemented in the form of a software functional module and stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, an essential part of the technical solution of the embodiments of the present application, i.e., a part that contributes to the related art, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing an electronic device (which may be a mobile phone, tablet computer, personal computer, personal digital assistant, navigator, digital telephone, video telephone, television, sensing device, server, etc.) to execute all or part of the method described in each embodiment of the present application. The storage medium includes various media capable of storing program code, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk. Thus, the embodiments of the present application are not limited to a specific combination of hardware and software.

[0152] In practical application, as shown in FIG. 9, a video content prediction device according to an embodiment of the present application comprises: a memory 34 configured to store executable video content prediction instructions; a processor 35 configured to implement the steps of the video component prediction method provided in the above embodiments when executing executable video component prediction instructions stored in the memory 34.

[0153] Correspondingly, an embodiment of the present application provides a computer-readable storage medium having video component prediction instructions stored thereon, which, when executed by the processor 35, implements the steps of the video component prediction method provided in the above embodiment.

[0154] It should be pointed out here that the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, and has the same beneficial effects as the method embodiments. Technical details not disclosed in the storage medium and device embodiments of the present application can be understood by referring to the description of the method embodiments of the present application.

[0155] The above content is merely a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any modifications or replacements that a person skilled in the art can easily think of within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims. [Industrial Applicability]

[0156] In an embodiment of the present application, the video component prediction device first determines a plurality of first image component reference values ​​based on a set of directly obtained reference values ​​of the first image component corresponding to the current block, and then performs a filtering process based on the positions corresponding to the determined plurality of first image component reference values ​​to construct a component linear model. In this way, the workload of the filtering process for the pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency.

Claims

1. 1. A method for predicting an image component, applied in a decoder, comprising: determining a reference sample set of a first image component of the current block; determining a plurality of first image component reference samples from the first image component reference sample set, the plurality of first image component reference samples corresponding to four preset sample locations; performing a first filtering process on the plurality of first image component reference samples respectively to obtain a plurality of filtered first image reference samples; determining a prediction-to-be-predicted image component reference sample corresponding to the plurality of filtered first image reference samples, wherein the prediction-to-be-predicted image component is an image component different from the first image component; determining parameters of a component linear model based on the plurality of filtered first image reference samples and the prediction-to-be-predicted image component reference samples, wherein the component linear model represents a linear mapping relationship that maps samples of the first image component to samples of the prediction-to-be-predicted image component; performing a mapping operation on the reconstructed samples of the first image component of the current block based on the component linear model to obtain mapped samples; determining a prediction sample of the prediction-to-be-predicted image component of the current block based on the mapping sample; Determining a plurality of first image component reference samples from the first image component reference sample set includes: determining the plurality of first image component reference samples from samples in one or more adjacent rows above the current block based on the four preset sample positions, or determining the plurality of first image component reference samples from samples in one or more adjacent columns to the left of the current block based on the four preset sample positions. Image component prediction method.

2. Determining the reference sample set of the first image component of the current block includes: determining neighboring samples of the current block as a reference sample set of the first image component; The image component prediction method according to claim 1 .

3. the neighboring samples of the current block include at least one of samples in one or more neighboring rows above the current block, and samples in one or more neighboring columns to the left of the current block; The image component prediction method according to claim 2 .

4. determining parameters of a component linear model based on the plurality of filtered first image reference samples and the to-be-predicted image component reference samples, determining a maximum filtered first image component reference sample and a minimum filtered first image component reference sample based on the plurality of filtered first image reference samples; determining a maximum prediction-awaiting image component reference sample corresponding to the maximum first image component reference sample after the filtering, and a minimum prediction-awaiting image component reference sample corresponding to the minimum first image component reference sample after the filtering; determining parameters of the component linear model based on the filtered maximum first image component reference sample, the filtered minimum first image component reference sample, the filtered maximum image component reference sample to be predicted, and the filtered minimum image component reference sample to be predicted; The image component prediction method according to claim 1 .

5. performing a mapping process on the reconstructed samples of the first image component of the current block based on the component linear model to obtain mapped samples; performing a second filtering operation on the reconstructed samples of the first image component to obtain second filtered samples of the reconstructed samples of the first image component; performing a mapping process on the second filtered samples based on the component linear model to determine the mapped samples. The image component prediction method according to claim 1 .

6. 1. A method for predicting an image component, applied to an encoder, comprising: determining a reference sample set of a first image component of the current block; determining a plurality of first image component reference samples from the first image component reference sample set, the plurality of first image component reference samples corresponding to four preset sample locations; performing a first filtering process on the plurality of first image component reference samples respectively to obtain a plurality of filtered first image reference samples; determining a prediction-to-be-predicted image component reference sample corresponding to the plurality of filtered first image reference samples, wherein the prediction-to-be-predicted image component is an image component different from the first image component; determining parameters of a component linear model based on the plurality of filtered first image reference samples and the prediction-to-be-predicted image component reference samples, wherein the component linear model represents a linear mapping relationship that maps samples of the first image component to samples of the prediction-to-be-predicted image component; performing a mapping operation on the reconstructed samples of the first image component of the current block based on the component linear model to obtain mapped samples; determining a prediction sample of the prediction-to-be-predicted image component of the current block based on the mapping sample; Determining a plurality of first image component reference samples from the first image component reference sample set includes: determining the plurality of first image component reference samples from samples in one or more adjacent rows above the current block based on the four preset sample positions, or determining the plurality of first image component reference samples from samples in one or more adjacent columns to the left of the current block based on the four preset sample positions. Image component prediction method.

7. Determining the reference sample set of the first image component of the current block includes: determining neighboring samples of the current block as a reference sample set of the first image component; The image component prediction method according to claim 6.

8. the neighboring samples of the current block include at least one of samples in one or more neighboring rows above the current block, and samples in one or more neighboring columns to the left of the current block; The image component prediction method according to claim 7 .

9. determining parameters of a component linear model based on the plurality of filtered first image reference samples and the to-be-predicted image component reference samples, determining a maximum filtered first image component reference sample and a minimum filtered first image component reference sample based on the plurality of filtered first image reference samples; determining a maximum prediction-awaiting image component reference sample corresponding to the maximum first image component reference sample after the filtering, and a minimum prediction-awaiting image component reference sample corresponding to the minimum first image component reference sample after the filtering; determining parameters of the component linear model based on the filtered maximum first image component reference sample, the filtered minimum first image component reference sample, the filtered maximum image component reference sample to be predicted, and the filtered minimum image component reference sample to be predicted; The image component prediction method according to claim 6.

10. performing a mapping process on the reconstructed samples of the first image component of the current block based on the component linear model to obtain mapped samples; performing a second filtering operation on the reconstructed samples of the first image component to obtain second filtered samples of the reconstructed samples of the first image component; performing a mapping process on the second filtered samples based on the component linear model to determine the mapped samples. The image component prediction method according to claim 6.

11. A decoding device, comprising: a memory configured to store executable video component prediction instructions; A processor configured to implement the image component prediction method of any one of claims 1 to 5 when executing executable video component prediction instructions stored in the memory.

12. 1. An encoding device, comprising: a memory configured to store executable video component prediction instructions; a processor configured to implement the method of any one of claims 6 to 10 when executing executable video component prediction instructions stored in the memory.

13. A non-volatile computer-readable medium on which a program and a bitstream are stored, the program causing a processor to execute an image component prediction method described in any one of claims 6 to 10 to generate the bitstream.

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