Method, apparatus, and computer storage medium for predicting image components

Abstract

To provide image component predicting method, device and computer storage medium that integrate a model parameter driving process without altering encoding / decoding prediction performance.SOLUTION: A method includes S501 obtaining a first reference pixel set corresponding to a predicted image component of a coding block in a video image, S502 using a preset component value as a predicted value corresponding to the predicted image component when the number of valid pixel points is less than the number of presets, S503 screening the first reference pixel set to obtain a second reference pixel set when the number of valid pixel points is larger than or equal to the number of presets, S504 using the preset component value as the predicted value corresponding to the predicted image component, and S505 implementing prediction processing for a predicted image component which corresponds to the predicted image component to be predicted by determining model parameters via the second reference pixel set, and obtaining a prediction model to be used to obtain the predicted value.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The embodiments of the present application relate to the field of video encoding and decoding technologies, and in particular, to a method and apparatus for predicting image components and a computer storage medium.

Background Art

[0002] With the increasing demands of people for video display quality, new forms of video applications such as high-resolution and ultra-high-resolution videos have emerged. H.265 / High Efficiency Video Coding (HEVC) cannot meet the requirements of the rapid development of video applications. The Joint Video Exploration Team (JVET) proposed the next-generation video coding standard H.266 / Versatile Video Coding (VVC), and the corresponding test model is the VVC Test Model (VTM).

[0003] In VTM, currently, a prediction method for image components based on a prediction model is integrated. Through this prediction model, the chrominance component can be predicted by the luminance component of the current coding block (CB). However, when constructing the prediction model, since the number of adjacent reference pixel points used for deriving the model parameters is different, not only is additional processing increased, but also the computational complexity is increased.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present embodiment provides a method, device, and computer storage medium for predicting image components, which integrates the model parameter derivation process without changing the encoding and decoding prediction performance. At the same time, if the number of valid pixel points in the neighboring reference pixel set is smaller than the preset number, no additional processing modules are added, so no additional processing is required and calculation complexity is reduced. [Means for solving the problem]

[0005] The technical solution of the present embodiment can be realized as follows.

[0006] In a first aspect, the present embodiments provide a method for predicting image components, the method comprising: Obtaining a first set of reference pixels corresponding to a predicted image component of a coding block in a video image; If the number of valid pixel points in the first reference pixel set is less than a preset number, using a preset component value as a predicted value corresponding to the predicted image component; if the number of valid pixel points in the first reference pixel set is greater than or equal to a preset number, screening the first reference pixel set to obtain a second reference pixel set, where the number of valid pixel points in the second reference pixel set is less than or equal to a preset number; If the number of valid pixel points in the second reference pixel set is smaller than the number of presets, using the preset component values as predicted values corresponding to the predicted image component; and if the number of valid pixel points in the second reference pixel set is equal to the number of presets, determining model parameters via the second reference pixel set and obtaining a prediction model corresponding to the predicted image component according to the model parameters, wherein the prediction model is used to realize a prediction process for the predicted image component and obtain a predicted value corresponding to the predicted image component.

[0007] In a second aspect, the embodiment of the present application provides a prediction apparatus for image components, comprising an acquisition unit, a prediction unit, and a screening unit, where the acquisition unit is configured to acquire a first set of reference pixel corresponding to a predicted image component of an encoded block in a video image; when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the prediction unit is configured to use the component value of the preset as a predicted value corresponding to the predicted image component; when the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, the screening unit is configured to screen the first set of reference pixels to obtain a second set of reference pixels, where the number of valid pixel points in the second set of reference pixels is smaller than or equal to the number of presets; when the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the prediction unit is further configured to use the component value of the preset as a predicted value corresponding to the predicted image component, and when the number of valid pixel points in the second set of reference pixels is equal to the number of presets, determine model parameters through the second set of reference pixels, and acquire a prediction model corresponding to the predicted image component according to the model parameters, where the prediction model is used to implement a prediction process for the predicted image component to obtain a predicted value corresponding to the predicted image component.

[0008] In a third aspect, the embodiment of the present application provides a prediction apparatus for image components, comprising a memory and a processor, the memory is configured to store a computer program executable by the processor; when executing the computer program, the processor is configured to execute the method described in the first aspect.

[0009] In a fourth aspect, the present application example provides a computer storage medium that stores an image component prediction program, and when the image component prediction program is executed by at least one processor, the method described in the first aspect is realized.

Effect of the Invention

[0010] Embodiments of the present application provide a method, apparatus, and computer storage medium for predicting an image component. By obtaining a first set of reference pixels corresponding to a predicted image component of an encoded block in a video image, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, the first set of reference pixels is screened to obtain a second set of reference pixels. The number of valid pixel points in the second set of reference pixels is less than or equal to the number of presets. When the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the second set of reference pixels is equal to the number of presets, model parameters are determined through the second set of reference pixels, and according to the model parameters, a prediction model corresponding to the predicted image component is obtained. The prediction model is used to perform a prediction process on the predicted image component to obtain a predicted value corresponding to the predicted image component. In this way, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, or when the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the default value of the preset is directly used as the predicted value corresponding to the predicted image component. Only when the number of valid pixel points in the second set of reference pixels meets the number of presets, model parameters are determined according to the first set of reference pixels to establish a prediction model for the predicted image component, thereby integrating the derivation process of the model parameters. Furthermore, when the number of valid pixel points in the first set of reference pixels or the second set of reference pixels is smaller than the number of presets, especially when the number of valid pixel points is 0 or 2, since no additional processing module is added, the default value of the preset is directly used as the predicted value corresponding to the predicted image component, and no additional processing is required, reducing the computational complexity.

Brief Description of the Drawings

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Mode for Carrying Out the Invention

[0012] To understand the features and technical content of the embodiments of the present application in more detail, hereinafter, the implementation of the embodiments of the present application will be described in detail with reference to the drawings. The attached drawings are for reference only and are not intended to limit the embodiments of the present application.

[0013] In a video image, usually, a coded block is represented using a first image component, a second image component, and a third image component. Here, these three image components are, respectively, one luminance component, one blue chrominance component, and one red chrominance component. Specifically, the luminance component is usually represented by the symbol Y, the blue chrominance component is usually represented by the symbol Cb or U, and the red chrominance component is usually represented by the symbol Cr or V. In this way, a video image can be represented in the YCbCr format or can also be represented in the YUV format.

[0014] In the embodiments of the present application, the first image component may be a luminance component, the second image component may be a blue chroma component, and the third image component may be a red chroma component, but in the embodiments of the present application, it is not particularly limited.

[0015] In the current video image or video encoding / decoding process, for the cross-component prediction technology, it mainly includes a cross-component linear model prediction (CCLM) mode and a multi-directional linear model prediction (MDLM) mode. Regardless of the model parameters derived according to the CCLM mode or the model parameters derived according to the MDLM mode, all corresponding prediction models can realize predictions between image components such as from the first image component to the second image component, from the second image component to the first image component, from the first image component to the third image component, from the third image component to the first image component, from the second image component to the third image component, or from the third image component to the second image component.

[0016] Taking the prediction from the first image component to the second image component as an example, in order to reduce the redundancy between the first image component and the second image component, the CCLM mode is used in VVC. In this case, the first image component and the second image component are from the same coding block, that is, according to the reconstructed value of the first image component of the same coding block, the predicted value of the second image component is constructed, as shown in the following formula (1).

Number

[0017] JPEG0007714766000002.jpg49170

[0018] For a symbolization block, its adjacent regions may include the left adjacent region, the upper adjacent region, the lower-left adjacent region, and the upper-right adjacent region. In VVC, it may include three cross-component linear model prediction modes, namely, the left and upper adjacent intra CCLM mode (which can be represented by the INTRA_LT_CCLM mode), the left and lower-left adjacent intra CCLM mode (which can be represented by the INTRA_L_CCLM mode), and the upper and upper-right adjacent intra CCLM mode (which can be represented by the INTRA_T_CCLM mode). In these three modes, each mode can select a preset number (e.g., 4) of adjacent reference pixel points for use in deriving the model parameters α and β. The biggest difference among these three modes is that the selection regions corresponding to the adjacent reference pixel points for deriving the model parameters α and β are different.

[0019] JPEG0007714766000003.jpg98170

[0020] JPEG0007714766000004.jpg106170

[0021] Referring to FIG. 1, a schematic diagram of the distribution of valid adjacent regions according to the embodiments of the present application is shown. In FIG. 1, the left adjacent region, the lower left adjacent region, the upper adjacent region, and the upper right adjacent region are all valid. Based on FIG. 1, the selection regions for the three modes are as shown in FIG. 2. Here, in FIG. 2, (a) shows the selection region for the INTRA_LT_CCLM mode, including the left adjacent region and the upper adjacent region, (b) shows the selection region for the INTRA_L_CCLM mode, including the left adjacent region and the lower left adjacent region, and (c) shows the selection region for the INTRA_T_CCLM mode, including the upper adjacent region and the upper right adjacent region. In this way, after determining the selection regions for the three modes, reference points for deriving model parameters can be selected in the selection regions. In this way, the selected reference points are referred to as adjacent reference pixel points. Usually, the number of adjacent reference pixel points is at most 4. For a coded block of size W×H determined by one size, the positions of its adjacent reference pixel points are usually determined.

[0022] However, for some special cases, such as the case of the boundary of the coded block, the case where it cannot be predicted, and the case where adjacent reference pixel points cannot be obtained according to the coding order, and further, when performing the division of the coded block according to tiles and slices, the adjacent regions may be invalid, whereby the number of adjacent reference pixel points selected from the adjacent regions becomes less than 4, that is, only 0 or 2 adjacent reference pixel points may be selected, whereby the number of adjacent reference pixel points used for deriving model parameters is not integrated, whereby while increasing the additional "duplication" operation, the computational complexity is increased.

[0023] On the premise that the symbolic and decoding prediction performance remains unchanged, the present application example provides a method for predicting image components by integrating the derivation process of model parameters and simultaneously reducing the computational complexity. By obtaining a first set of reference pixel corresponding to the predicted image components of the encoded blocks in the video image, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the component values of the presets are used as predicted values corresponding to the predicted image components. When the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, the first set of reference pixels is screened to obtain a second set of reference pixels, and the number of valid pixel points in the second set of reference pixels is smaller than or equal to the number of presets. When the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the component values of the presets are used as predicted values corresponding to the predicted image components. When the number of valid pixel points in the second set of reference pixels is equal to the number of presets, the model parameters are determined through the first set of reference pixels, and according to the model parameters, a prediction model corresponding to the predicted image components is obtained. Here, the prediction model is used to realize the prediction process for the predicted image components and obtain the predicted values corresponding to the predicted image components. In this way, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets or when the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the CCLM mode is disabled, and the default value of the preset is directly used as the predicted value corresponding to the predicted image components. Since no additional processing module is added, at the same time, the computational complexity is reduced. Furthermore, the derivation of model parameters is only executed when the number of valid pixel points in the second set of reference pixels is equal to the number of presets, that is, the CCLM mode is executed to unify the derivation process of model parameters.

[0024] The following will describe each embodiment of the present application in detail with reference to the drawings.

[0025] Referring to FIG. 3, an exemplary block diagram of the configuration of a video encoding system according to an embodiment of the present application is shown. As shown in FIG. 3, the video encoding system 300 includes a transform and quantization unit 301, an intra prediction unit 302, an intra prediction unit 303, a motion compensation unit 304, a motion estimation unit 305, an inverse transform and inverse quantization unit 306, a filter control analysis unit 307, a filtering unit 308, an encoding unit 309, a decoded image cache unit 310, etc. Here, the filtering unit 308 can implement deblocking filtering and sample adaptive offset (SAO) filtering, and the encoding unit 309 can implement header encoding and context-based adaptive binary arithmetic coding (CABAC). For the input original video signal, one video coding block can be obtained through the division of a coding tree block (CTU). Then, for the residual pixel information obtained by intra or inter prediction, the video coding block is transformed through the transform and quantization unit 301, the residual information is transformed from the pixel field to the transform field, and the obtained transform coefficients are quantized to reduce the bit rate.The intra-estimation unit 302 and the intra-prediction unit 303 are configured to perform intra-prediction on the video coding block. Specifically, the intra-estimation unit 302 and the intra-prediction unit 303 are configured to determine the intra-prediction mode used to code the video coding block. The motion compensation unit 304 and the motion estimation unit 305 are configured to perform inter-prediction coding of the received video coding block corresponding to one or more blocks in one or more reference frames to provide temporal prediction information. The motion estimation performed by the motion estimation unit 305 is a process of generating a motion vector, and the motion vector can estimate the motion of the video coding block. Subsequently, the motion compensation unit 304 performs motion compensation based on the motion vector determined by the motion estimation unit 305. After determining the intra-prediction mode, the intra-prediction unit 303 is further configured to provide the selected intra-prediction data to the encoding unit 309, and the motion estimation unit 305 also transmits the calculated and determined motion vector data to the encoding unit 309. Note that the inverse transform and inverse quantization unit 306 is configured to reconstruct the video coding block and reconstruct the residual block in the pixel field. The reconstructed residual block removes block effect artifacts through the filter control analysis unit 307 and the filtering unit 308, and then adds the reconstructed residual block to one prediction block in the frame of the decoded image cache unit 310 to generate the reconstructed video coding block.The symbolization unit 309 is configured to code various coding parameters and quantized transform coefficients. In the CABAC-based coding algorithm, the context content can be based on adjacent coding blocks, code the information of the intra prediction mode determined by an instruction, and is configured to output the bitstream of the video signal. The decoded picture cache unit 310 is configured to store the video coding blocks to be reconstructed for reference prediction. As the execution of video picture coding continues to generate new reconstructed video coding blocks, all of these reconstructed video coding blocks are stored in the decoded picture cache unit 310.

[0026] Referring to FIG. 4, an exemplary block diagram of the configuration of the video decoding system according to the embodiment of the present application is shown. As shown in FIG. 4, the video decoding system 400 includes a decoding unit 401, an inverse transform and inverse quantization unit 402, an intra prediction unit 403, a motion compensation unit 404, a filtering unit 405, and a decoded image cache unit 406. Here, the decoding unit 401 can implement header decoding and CABAC decoding, and the filtering unit 405 can implement deblocking filtering and SAO filtering. The input video signal outputs the bitstream of the video signal after performing the coding process of FIG. 2. The bitstream is input into the video decoding system 400. To obtain the decoded transform coefficients, it first passes through the decoding unit 401 and is processed through the inverse transform and inverse quantization unit 402 for the transform coefficients to generate a residual block in the pixel field. The intra prediction unit 403 is configured to generate prediction data for the current video decoding block based on the determined intra prediction mode and the data passing through the decoding block from the current frame or picture. The motion compensation unit 404 is configured to determine prediction information for the video decoding block by analyzing the motion vector and other related syntax elements, and use the prediction information to generate a prediction block for the video decoding block being decoded. The residual block from the inverse transform and inverse quantization unit 402 is added to the corresponding prediction block generated by the intra prediction unit 403 or the motion compensation unit 404 to form a decoded video block. The decoded video signal passes through the filtering unit 405 to remove block effect artifacts and improve video quality. Then, the decoded video block is stored in the decoded image cache unit 406. The decoded image cache unit 406 is configured to store a reference image for subsequent intra prediction or motion compensation and output the video signal, that is, to obtain the original video signal to be restored.

[0027] The method for predicting an image component in an embodiment of the present application is mainly applied to the part of the intra prediction unit 303 shown in FIG. 3 and the part of the intra prediction unit 403 shown in FIG. 4. Specifically, it is applied to the CCLM prediction unit in intra prediction. That is, the method for predicting an image component in an embodiment of the present application may be applied to a video encoding system, may be applied to a video decoding system, and further may be applied to both the video encoding system and the video decoding system at the same time, but is not particularly limited in the embodiments of the present application. When the method is applied to the part of the intra prediction unit 303, the "encoded block in the video image" specifically refers to the current encoded block in intra prediction. When the method is applied to the part of the intra prediction unit 403, the "encoded block in the video image" specifically refers to the current decoded block in intra prediction.

[0028] Based on the above example of the application scenario of FIG. 3 or FIG. 4, referring to FIG. 5, an exemplary flowchart of the method for predicting an image component according to an embodiment of the present application is shown. As shown in FIG. 5, the method may include the following steps.

[0029] In S501, a first reference pixel set corresponding to the predicted image component of the encoded block in the video image is obtained.

[0030] The video image can be divided into a plurality of encoded blocks, and each encoded block may include a first image component, a second image component, and a third image component. It should be noted that the encoded block in an embodiment of the present application is the current block being encoded in the video image. When it is necessary to predict the first image component through a prediction model, the predicted image component is the first image component. When it is necessary to predict the second image component through a prediction model, the predicted image component is the second image component. When it is necessary to predict the third image component through a prediction model, the predicted image component is the third image component.

[0031] Furthermore, when the left adjacent region, the lower left adjacent region, the upper adjacent region, and the upper right adjacent region are all valid regions, for the INTRA_LT_CCLM mode, as shown in FIG. 2(a), the first reference pixel set is composed of adjacent reference pixel points in the left adjacent region and the upper adjacent region of the coding block; for the INTRA_L_CCLM mode, as shown in FIG. 2(b), the first reference pixel set is composed of adjacent reference pixel points in the left adjacent region and the lower left adjacent region of the coding block; for the INTRA_T_CCLM mode, it should be noted that the first reference pixel set is formed by adjacent reference pixel points in the upper adjacent region and the upper right adjacent region of the coding block, as shown in FIG. 2(c).

[0032] In some embodiments, by way of example, for S501, obtaining the first reference pixel set corresponding to the predicted image component of the coding block in the video image may include the following steps.

[0033] In S501a-1, obtain reference pixel points adjacent to at least one side of the coding block, where the at least one side includes the left side of the coding block and / or the upper side of the coding block.

[0034] In S501a-2, form the first reference pixel set corresponding to the predicted image component based on the reference pixel points.

[0035] It should be noted that at least one side of the coding block may include the left side of the coding block and / or the upper side of the coding block, that is, at least one side of the coding block may refer to the upper side of the coding block, may refer to the left side of the coding block, or may further refer to the upper side and the left side of the coding block, and is not particularly limited in the embodiments of the present application.

[0036] In this way, for the INTRA_LT_CCLM mode, when the left adjacent region and the upper adjacent region are all valid regions, the first reference pixel set can be formed by the reference pixel points adjacent to the left side of the coding block and the reference pixel points adjacent to the upper side of the coding block. When the left adjacent region is a valid region and the upper adjacent region is an invalid region, the first reference pixel set can be formed by the reference pixel points adjacent to the left side of the coding block. When the left adjacent region is an invalid region and the upper adjacent region is a valid region, the first reference pixel set can be formed by the reference pixel points adjacent to the upper side of the coding block.

[0037] In some embodiments, by way of example, for S501, obtaining the first reference pixel set corresponding to the predicted image component of the coding block in the video image may include the following steps.

[0038] In S501b-1, obtain the reference pixel points in the reference row or reference column adjacent to the coding block, where the reference row is formed by the rows adjacent to the upper side and the upper right side of the coding block, and the reference column is formed by the columns adjacent to the left side and the lower left side of the coding block.

[0039] In S501b-2, based on the reference pixel points, form the first reference pixel set corresponding to the predicted image component.

[0040] The reference lines adjacent to the encoding block can be formed by the lines adjacent to the upper side and the upper right side of the encoding block, and the reference columns adjacent to the encoding block can be formed by the columns adjacent to the left side and the lower left side of the encoding block. The reference line or reference column adjacent to the encoding block may refer to the reference line adjacent to the upper side of the encoding block, or may refer to the reference column adjacent to the left side of the encoding block. Furthermore, it may refer to the reference line or reference column adjacent to other edges of the encoding block, and it should be noted that there is no particular limitation in the embodiments of the present application. For the convenience of description, in the embodiments of the present application, the reference line adjacent to the encoding block will be described by taking the reference line adjacent to the upper side as an example, and the reference column adjacent to the encoding block will be described by taking the reference column adjacent to the left side as an example.

[0041] Here, the reference pixel points in the reference line adjacent to the encoding block may include the reference pixel points adjacent to the upper side and the upper right side (which may also be referred to as the adjacent reference pixel points corresponding to the upper side and the upper right side). Here, the upper side represents the upper side of the encoding block, and the upper right side represents the length of the side that extends horizontally to the right from the upper side of the encoding block and has the same height as the current encoding block. The reference pixel points in the reference column adjacent to the encoding block may further include the reference pixel points adjacent to the left side and the lower left side (which may also be referred to as the adjacent reference pixel points corresponding to the left side and the lower left side). Here, the left side represents the left side of the encoding block, and the lower left side represents the length of the side that extends vertically downward from the left side of the encoding block and has the same width as the current decoding block, but there is no particular limitation in the embodiments of the present application.

[0042] In this way, for the INTRA_L_CCLM mode, when the left adjacent region and the lower left adjacent region are valid regions, the first reference pixel set can be formed by the reference pixel points in the reference column adjacent to the encoding block. For the INTRA_T_CCLM mode, when the upper adjacent region and the upper right adjacent region are valid regions, the first reference pixel set can be formed by the reference pixel points in the reference line adjacent to the encoding block.

[0043] In S502, when the number of valid pixel points in the first reference pixel set is smaller than the number of presets, the component values of the presets are used as the predicted values corresponding to the predicted image components.

[0044] It should be noted that the number of valid pixel points can be determined according to the validity of the adjacent region and can be determined according to the number of valid pixel points in the selected region. For some special cases, for example, in the case of the boundary of the coding block, when prediction is not possible, and when adjacent reference pixel points cannot be obtained according to the coding order, and further, when the coding block is divided according to tile and slice, the left adjacent region, the lower left adjacent region, the upper adjacent region, and the upper right adjacent region are not all valid regions and may have invalid regions, whereby the number of valid pixel points in the selected region is smaller than the number of presets, making the number of valid pixel points in the first reference pixel set smaller than the number of presets.

[0045] Furthermore, the number of presets is a determined value of the number of preset valid pixel points and is used to measure whether the predicted image component executes the steps of deriving model parameters and constructing a prediction model. Here, it should be noted that the number of presets can be four, but is not particularly limited in the embodiments of the present application. In this way, assuming that the number of presets is four, that is, when the number of valid pixel points in the first reference pixel set is 0 or 2, the component values of the presets are directly used as the predicted values corresponding to the predicted image components to reduce the computational complexity.

[0046] Furthermore, the preset component values are used to represent fixed values (which may also be referred to as default values) corresponding to the predicted image components set in advance. Here, the preset component values mainly relate to the bit information of the current video image. Therefore, in some embodiments, for S502, when the number of valid pixel points in the first reference pixel set is smaller than the number of presets, using the preset component values as the predicted values corresponding to the predicted image components may include the following steps.

[0047] In S502a, based on the bit information of the video image, determine the preset component range corresponding to the predicted image component.

[0048] In S502b, according to the preset component range, determine the median value of the preset component range, and use the median value as the predicted value corresponding to the predicted image component. Here, the median value is indicated by the preset component value.

[0049] It should be noted that in the embodiments of the present application, the median value of the preset component range corresponding to the predicted image component can be used as the preset component value, and then it can be used as the predicted value corresponding to the predicted image component. Here, assuming that the bit depth of the predicted image component is represented by BitDepthC, it can be obtained that the calculation method of the median value of the predicted image component is 1<<(BitDepthC - 1), and this calculation method can be set according to the actual situation and is not particularly limited in the embodiments of the present application.

[0050] Exemplarily, taking the case where the predicted image component is a chrominance component as an example, assuming that the current video image is an 8-bit video, the component range corresponding to the chrominance component is 0 to 255, where the median value is 128. In this case, the preset component value can be 128, that is, the default value can be 128. Assuming that the current video image is a 10-bit video, the component range corresponding to the chrominance component is 0 to 1023, where the median value is 512. In this case, the preset component value can be 512, that is, the default value is 512. In the embodiments of the present application, the bit information of the video image is taken as an example of 10 bits, that is, the preset component value is 512.

[0051] Furthermore, in some embodiments, after using the preset component value as the predicted value corresponding to the predicted image component for S502, the method may further include the following steps.

[0052] In S502c, for each pixel point in the encoded block, use the preset component value to perform filling of the predicted value for the predicted image component of each pixel point.

[0053] It should be noted that when the number of valid pixel points in the first reference pixel set is smaller than the preset number, without the need to increase an additional processing module, a fixed default value can be directly used to perform filling of the predicted value for the predicted image component in the encoded block.

[0054] Exemplarily, assuming that the preset component value is 512 and the predicted image component is a chrominance component, for the chrominance prediction value corresponding to each pixel point in the encoded block, 512 can be directly used to fill the chrominance prediction value.

[0055] In S503, when the number of valid pixel points in the first reference pixel set is greater than or equal to the preset number, screen the first reference pixel set to obtain a second reference pixel set.

[0056] Within the first parameter pixel set, there may be some reference pixel points that are not important (for example, their relevance is poor) or some abnormal reference pixel points. To ensure the accuracy of the prediction model, it is necessary to delete these reference pixel points to obtain a second reference pixel set. It should be noted that the number of valid pixel points in the second reference pixel set here is less than or equal to the number of presets. Here, the number of valid pixel points included in the second reference pixel set, in actual application, the number of presets is usually selected to be 4, but it is not particularly limited in the embodiments of the present application.

[0057] Furthermore, when the number of valid pixel points in the first reference pixel set is greater than or equal to the number of presets, the first reference pixel set can also be screened to obtain a second reference pixel set. It should be noted that after obtaining the second reference pixel set, it is still necessary to judge according to the number of valid pixel points in the second reference pixel set and the number of presets. Here, when the number of valid pixel points in the second reference pixel set is less than the number of presets, the component values of the presets can be adopted and used as the predicted values corresponding to the predicted image components. When the number of valid pixel points in the second reference pixel set is equal to the number of presets, the model parameters can be derived according to the second reference pixel set.

[0058] Furthermore, in some embodiments, for S503, screening the first reference pixel set to obtain a second reference pixel set may include determining the selected pixel point positions based on the pixel positions and / or image component intensities corresponding to each adjacent reference pixel point in the first reference pixel set, and selecting the valid pixel points corresponding to the selected pixel point positions from the first reference pixel set according to the determined selected pixel point positions, and forming a second reference pixel set with the selected valid pixel points. Here, the number of valid pixel points in the second reference pixel set is less than or equal to the number of presets.

[0059] Specifically, for the screening of the first reference pixel set, it may be screened according to the position of the selected reference pixel points, or it may be screened according to the image component intensity (such as luminance value, chrominance value), whereby the second reference pixel set is formed by the screened selected reference pixel points. The following will explain by taking the position of the selected reference pixel points as an example.

[0060] Assuming that the number of valid pixel point samples in the upper region and the upper-right region adjacent to the current coding block is numSampT, and the number of valid pixel point samples in the left region and the lower-left region adjacent to the current coding block is numSampL, the screening process is as follows (here, availT represents the validity of the row adjacent to the current coding block above, availL represents the validity of the column adjacent to the current coding block on the left, nTbW represents the width of the current coding block, and nTbH represents the height of the current coding block).

[0061] When the intra prediction mode of the current block is the INTRA_LT_CCLM mode, numSampT = availT? nTbW : 0 numSampL = availL? nTbH : 0, and otherwise, numSampT=(availT&&predModeIntra==INTRA_T_CCLM)?(nTbW+Min(numTopRight,nTbH)):0 numSampL=(availL&&predModeIntra==INTRA_L_CCLM)?(nTbH+Min(numLeftBelow,nTbW)):0.

[0062] Here, numTopRight represents the number of valid pixel points within the upper - right nTbW range, and numLeftBelow represents the number of valid pixel points within the lower - left nTbH. The number of pixel points screened at each edge is indicated by cntN, the starting point position is indicated by startPosN, the selection interval is indicated by pickStepN, the selected pixel point position is indicated by pickPosN[pos], and its derivation process is as follows.

[0063] The variable numIs4N represents whether to screen pixel points on one side: numIs4N = ((availT && availL && predModeIntra == INTRA_LT_CCLM)? 0 : 1) The variable startPosN represents the starting point position: startPosN = numSampN >> (2 + numIs4N) The variable pickStepN represents the selection interval: pickStepN = Max(1, numSampN >> (1 + numIs4N)) Here, N is replaced by T and L respectively, representing the cases of screening pixel points on the upper side and the left side respectively, that is, the N - side here represents the T - side or the L - side. Here, when the validity availN of the N - side is TRUE and the selected intra - mode predModeIntra is in the INTRA_LT_CCLM mode or the INTRA_N_CCLM mode, the number of pixel points cntN screened on the N - side and the selected pixel point position pickPosN[pos] are as follows (it should be noted that the total number of screened pixel points should be cntT + cntL).

[0064] cntN = Min(numSampN, (1 + numIs4N) << 1) pickPosN[pos] = (startPosN + pos * pickStepN), with pos = 0...cntN - 1 Otherwise, set cntN to 0, that is, the number of screened pixel points is 0.

[0065] Assuming that the prediction samples of the current coded block are predSamples[x][y] with x = 0...nTbW-1, y = 0...nTbH-1, its derivation is as follows.

[0066] If both numSampL and numSampT are invalid, set them to preset values, which are as follows.

[0067] predSamples[x][y] = 1 << (BitDepth C -1) Otherwise, In the first step, obtain the luminance reconstruction samples pY[x][y] of the co-located luminance block with x = 0...nTbW*2-1, y = 0...nTbH*2-1. In the second step, obtain the adjacent luminance reconstruction samples pY[x][y]. In the third step, obtain the downsampled luminance reconstruction samples pDsY[x][y] with x = 0...nTbW-1, y = 0…nTbH-1. In the fourth step, if numSampL is greater than 0, set the chrominance values pSelC[idx] of the points selected by the left side to p[-1][pickPosL[idx]] with idx = 0...cntL-1, and obtain the downsampled reconstructed luminance values pSelDsY[idx] of the points selected by the left side with idx = 0...cntL-1.

[0068] In the fifth step, if numSampT is greater than 0, set the chrominance values pSelC[idx] of the points selected by the top side to p[pickPosT[idx-cntL]][-1] with idx = cntL...cntL+cntT-1, and obtain the downsampled reconstructed luminance values pSelDsY[idx] of the points selected by the top side with idx = 0…cntL+cntT-1.

[0069] In step 6, when cntT + cntL is different from 0, the derivation of the variables minY, maxY, minC and maxC is as follows.

[0070] When cntT + cntL is equal to 2, set pSelComp[3] to pSelComp[0], pSelComp[2] to pSelComp[1], pSelComp[0] to pSelComp[1], and pSelComp[1] to pSelComp[3], where Comp is replaced by DsY and C respectively to represent the reconstructed luminance and chrominance of the selected adjacent samples.

[0071] The derivation of the arrays minGrpIdx and maxGrpIdx is as follows.

[0072] minGrpIdx[0] = 0 minGrpIdx[1] = 2 maxGrpIdx[0] = 1 maxGrpIdx[1] = 3 If pSelDsY[minGrpIdx[0]] is greater than pSelDsY[minGrpIdx[1]], then minGrpIdx[0] is exchanged with minGrpIdx[1], (minGrpIdx[0], minGrpIdx[1]) = Swap(minGrpIdx[0], minGrpIdx[1]) If pSelDsY[maxGrpIdx[0]] is greater than pSelDsY[maxGrpIdx[1]], then maxGrpIdx[0] is exchanged with maxGrpIdx[1], (maxGrpIdx[0], maxGrpIdx[1]) = Swap(maxGrpIdx[0], maxGrpIdx[1]) If pSelDsY[minGrpIdx[0]] is greater than pSelDsY[maxGrpIdx[1]], the array minGrpIdx is exchanged with maxGrpIdx, (minGrpIdx, maxGrpIdx) = Swap(minGrpIdx, maxGrpIdx) If pSelDsY[minGrpIdx[1]] is greater than pSelDsY[maxGrpIdx[0]], then minGrpIdx[1] is exchanged with maxGrpIdx[0], (minGrpIdx[1],maxGrpIdx[0])=Swap(minGrpIdx[1],maxGrpIdx[0]) The calculation of the variables maxY, maxC, minY and minC is as follows (representing the average value of two groups respectively).

[0073] maxY=(pSelDsY[maxGrpIdx[0]]+pSelDsY[maxGrpIdx[1]]+1)>>1 maxC=(pSelC[maxGrpIdx[0]]+pSelC[maxGrpIdx[1]]+1)>>1 minY=(pSelDsY[minGrpIdx[0]]+pSelDsY[minGrpIdx[1]]+1)>>1 minC=(pSelC[minGrpIdx[0]]+pSelC[minGrpIdx[1]]+1)>>1 In the seventh step, the derivation process of the linear model parameters a, b and k is as follows (where a is the slope (the ratio of the difference in chrominance to the difference in luminance), b is the offset, k is the shift of a, and a is stored as an integer).

[0074] If numSampL is equal to 0 and numSampT is equal to 0, k = 0 a = 0 b = 1<<(BitDepth C -1) Otherwise, diff = maxY - minY If diff is different from 0, diffC = maxC - minC x = Floor(Log2(diff)) normDiff = ((diff<<4)>>x)&15 x += (normDiff!= 0)? 1 : 0 y = Floor(Log2(Abs(diffC))) + 1 a = (diffC * (divSigTable[normDiff] | 8) + 2 * y - 1) >> y k = ((3 + x - y) < 1)? 1 : 3 + x - y a = ((3 + x - y) < 1)? Sign(a) * 15 : a b = minC - ((a * minY) >> k) Here, divSigTable[] = {0, 7, 6, 5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 0}, Otherwise (when diff is equal to 0), k = 0 a = 0 b = minC In the 8th step, the chrominance prediction samples predSamples[x][y] with x = 0...nTbW - 1, y = 0...nTbH - 1 are obtained through the following calculation (where Clip1C limits the predicted value between 0 - 1023), predSamples[x][y] = Clip1C(((pDsY[x][y] * a) >> k) + b)

[0075] JPEG0007714766000005.jpg49170

[0076] JPEG0007714766000006.jpg99170

[0077] JPEG0007714766000007.jpg85170

[0078] JPEG0007714766000008.jpg86170

[0079] In this way, when the number of valid pixel points in the first reference pixel set is greater than or equal to the number of presets, the second reference pixel set can be obtained by screening the first reference pixel set, and the second reference pixel set contains 4 valid pixel points.

[0080] In S504, when the number of valid pixel points in the second reference pixel set is smaller than the number of presets, the component values of the presets are used as predicted values corresponding to the predicted image components.

[0081] In S505, when the number of valid pixel points in the second reference pixel set is equal to the number of presets, model parameters are determined via the second reference pixel set, and a prediction model corresponding to the predicted image components is obtained according to the model parameters.

[0082] It should be noted that after screening the first reference pixel set, the second reference pixel set is obtained. The number of valid pixel points in the second reference pixel set may be smaller than the number of presets, may be larger than or equal to the number of presets. When the number of valid pixel points in the second reference pixel set is smaller than the number of presets, the component values of the presets are directly used as predicted values corresponding to the predicted image components. When the number of valid pixel points in the second reference pixel set is larger than or equal to the number of presets, model parameters are determined via the second reference pixel set, and a prediction model corresponding to the predicted image components is obtained according to the model parameters. Note that since the reference pixel points used for deriving the model parameters are usually four, the second reference pixel set obtained by screening is smaller than the number of presets (the second reference pixel set is smaller than four valid pixel points) or equal to the number of presets (the second reference pixel set includes four valid pixel points).

[0083] It should be noted that the prediction model may be a linear model or a non - linear model. Here, the non - linear model may be in a non - linear form such as a quadratic curve, or may be in a non - linear form formed by a plurality of linear models. For example, the cross - component prediction technology of the multi - model CCLM (MMLM: Multiple Model CCLM) is in a non - linear form formed by a plurality of linear models, and is not particularly limited in the embodiments of the present application. Here, the prediction model can be used to perform a prediction process on the predicted image component to obtain a predicted value corresponding to the predicted image component.

[0084] After obtaining the second reference pixel set, when the number of valid pixel points in the second reference pixel set is equal to the number of presets, the model parameters can be determined according to the second reference pixel set. After deriving the model parameters, further, as shown in formula (1), a prediction model corresponding to the chrominance component can be obtained according to the model parameters, and then, using the prediction model, a prediction process is performed on the chrominance component to obtain a predicted value corresponding to the chrominance component.

[0085] Furthermore, in some embodiments, for S505, after obtaining a prediction model corresponding to the predicted image component according to the model parameters, the method may further include performing a prediction process on the predicted image component of each pixel point in the encoded block based on the prediction model to obtain a predicted value corresponding to the predicted image component of each pixel point.

[0086] When the number of valid pixel points in the first reference pixel set is greater than or equal to the number of presets, in order to obtain the predicted value corresponding to the predicted image component of each pixel point in the coding block, it is necessary to determine the model parameters (for example, α and β) via the first reference pixel set, and then, it should be noted that it is necessary to obtain a prediction model corresponding to the predicted image component according to the model parameters. For example, assuming that the predicted image component is a chrominance component, according to the model parameters α and β, a prediction model corresponding to the chrominance component shown in Equation (1) can be obtained, and then, using the prediction model shown in Equation (1), prediction processing is performed on the chrominance component of each pixel point in the coding block, and in this way, the predicted value corresponding to the chrominance component of each pixel point can be obtained.

[0087] In the embodiment of the present application, for the left adjacent region, the lower left adjacent region, the upper adjacent region, and the upper right adjacent region, there may be a valid region and there may also be an invalid region, whereby the number of valid pixel points selected from the adjacent regions may be smaller than the number of presets. Therefore, in some embodiments, referring to FIG. 7, an exemplary flowchart of another method for predicting an image component according to the embodiment of the present application is shown. As shown in FIG. 7, after S501, the method may further include the following steps.

[0088] In S701, determine the number of valid pixel points in the first reference pixel set, and determine whether the number of the valid pixel points is smaller than the number of presets.

[0089] Furthermore, after S503, the method may further include the following steps.

[0090] In S702, determine whether the number of valid pixel points in the second reference pixel set is smaller than the number of presets.

[0091] It should be noted that the determination of the number of valid pixel points can be judged according to the validity of the adjacent area. In this way, after determining the number of valid pixel points, by comparing the number of valid pixel points with the number of presets, if the number of valid pixel points in the first reference pixel set is smaller than the number of presets, step S502 is executed; if the number of valid pixel points in the second reference pixel set is smaller than the number of presets, step S504 is executed; if the number of valid pixel points in the second reference pixel set is greater than or equal to the number of presets, step S505 is executed.

[0092] Furthermore, it should be noted that the number of presets can be four. The following will be described in detail by taking the number of presets being four as an example.

[0093] In one possible embodiment, when the number of valid pixel points in the first reference pixel set is greater than or equal to the number of presets, since the number of reference pixel points used for deriving the model parameters is usually four, first, the first reference pixel set is screened to make the number of valid pixel points in the first reference pixel set four. Then, according to these four valid pixel points, the model parameters are derived, and according to the model parameters, a prediction model corresponding to the predicted image component can be obtained, and a predicted value corresponding to the predicted image component can be obtained.

[0094] Specifically, assume that the predicted image component is a chrominance component and the chrominance component is predicted via a luminance component. Assume that the numbers of the four effective pixel points selected by screening are 0, 1, 2, and 3, respectively. By comparing these four selected effective pixel points, based on four comparisons, further, two pixel points with relatively large luminance values (which may include the pixel point with the largest luminance value and the pixel point with the second largest luminance value) and two pixel points with relatively small luminance values (which may include the pixel point with the smallest luminance value and the pixel point with the second smallest luminance value) can be selected. Further, set two arrays of minIdx[2] and maxIdx[2] to store two groups of pixel points respectively. Initially, first, put the effective pixel points numbered 0 and 2 into minIdx[2] and the effective pixel points numbered 1 and 3 into maxIdx[2] as follows.

[0095] Init:minIdx[2]={0,2},maxIdx[2]={1,3} Thereafter, through four comparisons, it can be ensured that what is stored in minIdx[2] are two pixel points with relatively small luminance values and what is stored in maxIdx[2] are two pixel points with relatively large luminance values. Specifically, it is as follows.

[0096] Step1:if(L[minIdx[0]]>L[minIdx[1]],swap(minIdx[0],minIdx[1]) Step2:if(L[maxIdx[0]]>L[maxIdx[1]],swap(maxIdx[0],maxIdx[1]) Step3:if(L[minIdx[0]]>L[maxIdx[1]],swap(minIdx,maxIdx) Step4:if(L[minIdx[1]]>L[maxIdx[0]],swap(minIdx[1],maxIdx[0]) In this way, two pixel points with relatively small luminance values can be obtained, and the corresponding luminance values are respectively luma 0 min and luma 1 minas shown, and the corresponding chroma values are respectively chroma 0 min and chroma 1 min as shown. At the same time, two pixel points with relatively large luminance values can be obtained, and the corresponding luminance values are respectively luma 0 max and luma 1 max as shown, and the corresponding chroma values are respectively chroma 0 max and chroma 1 max as shown. Further, an average value calculation can be performed on the luminance values corresponding to two relatively small pixel points to obtain the luminance value corresponding to the first average value point, which is shown as luma min as shown. An average value calculation can be performed on the luminance values corresponding to two relatively large pixel points to obtain the luminance value corresponding to the second average value point, which is shown as luma max as shown. Similarly, the chroma values corresponding to the two average value points can also be obtained, which are respectively chroma min and chroma max as shown. Specifically, it is as follows.

[0097] luma min =(luma 0 min +luma 1 min +1)>>1 luma max =(luma 0 max +luma 1 max +1)>>1 chroma min =(chroma 0 min +chroma 1 min +1)>>1 chroma max =(chroma 0 max +chroma 1 max +1)>>1 That is, the two average value points (luma min , chroma minand (luma max , chroma max ) After obtaining, the model parameters can be obtained by the calculation method of "two points determine a straight line" according to these two points. Specifically, the model parameters α and β can be calculated by Equation (2).

Equation

[0098] Here, the model parameter α is the slope in the prediction model, and the model parameter β is the intercept in the prediction model. In this way, after deriving the model parameters, as shown in Equation (1), a prediction model corresponding to the chrominance component can be obtained according to the model parameters, and then, using the prediction model, prediction processing is performed on the chrominance component to obtain a predicted value corresponding to the chrominance component.

[0099] In another possible embodiment, for some special cases, for example, in the case of the boundary of the coding block, when it cannot be predicted, and when adjacent reference pixel points cannot be obtained according to the coding order, etc., further, when performing the division of the coding block according to the tile and slice, the left adjacent region, the lower left adjacent region, the upper adjacent region, and the upper right adjacent region are not all valid regions, and there may be invalid regions, thereby reducing the number of valid pixel points in the first reference pixel set to be less than the preset number.

[0100] In this way, since the number of presets is 4, in some embodiments, for S502, when the number of valid pixel points in the first reference pixel set is less than the number of presets, using the component value of the preset as the predicted value corresponding to the predicted image component is When the number of valid pixel points in the first reference pixel set is 0 or 2, it may include using the component value of the preset as the predicted value corresponding to the predicted image component.

[0101] In some embodiments, for S504, when the number of valid pixel points in the current second reference pixel set is smaller than the number of presets, using the component values of the presets as the predicted values corresponding to the predicted image components is When the number of valid pixel points in the second reference pixel set is 0 or 2, it may include using the component values of the presets as the predicted values corresponding to the predicted image components.

[0102] That is, when the number of presets is 4, even if the number of valid pixel points in the first reference pixel set is smaller than the number of presets, and even if the number of valid pixel points in the second reference pixel set is smaller than the number of presets, the number of valid pixel points is 0 or 2.

[0103] Specifically, when the sum of adjacent reference pixel points in the selection area used for the coding block is 0, 0 valid pixel points are selected. In the following three special cases, 0 valid pixel points are obtained.

[0104] JPEG0007714766000010.jpg33170

[0105] JPEG0007714766000011.jpg34170

[0106] JPEG0007714766000012.jpg34170

[0107] Furthermore, as should be noted, the fact that the number of valid pixel points is 0 is determined according to the validity of the adjacent area, that is, according to the validity of the adjacent area, the number of valid pixel points in the first reference pixel set can be determined. When the number of valid pixel points is 0, the model parameter α can be set to 0, and the model parameter β can be set to the component values of the presets corresponding to the predicted image components.

[0108] JPEG0007714766000013.jpg80170

[0109] JPEG0007714766000014.jpg59170

[0110] JPEG0007714766000015.jpg46170

[0111] JPEG0007714766000016.jpg40170

[0112] JPEG0007714766000017.jpg41170

[0113] Furthermore, it should be noted that the fact that there are two valid pixel points can be determined according to the validity of the adjacent region, can be determined according to the number of valid pixel points in the selected region, and furthermore, can be determined according to other determination conditions, and is not particularly limited in the embodiments of the present application. In this way, according to the validity of the adjacent region, the number of valid pixel points in the first reference pixel set can be determined.

[0114] In the technical solution of the prior art, when there are two valid pixel points, it is necessary to obtain four pixel points by replicating the two valid pixel points. Exemplarily, assuming that the numbers of the four pixel points are 0, 1, 2, and 3, number 0: the second selected valid pixel point, number 1: the first selected valid pixel point, number 2: the second selected valid pixel point, number 3: the first selected valid pixel point, according to the four pixel points with numbers 0, 1, 2, and 3, the model parameters α and β can be determined, and the prediction model shown in formula (1) can be established, and through the prediction model, the predicted value corresponding to the predicted image component can be obtained.

[0115] JPEG0007714766000018.jpg75170

[0116] Therefore, in the technical solution of the prior art, when the number of effective pixel points is two, an additional "duplication" operation is required to use the same processing module, obtaining four pixel points, and thereby deriving model parameters according to the same operation process when the number of effective pixel points is four, and the additional "duplication" operation can be increased. The four obtained pixel points still need to perform calculations of four comparisons and taking four average values, thereby making the calculation complexity relatively high. In the embodiments of the present application, the processing when the number of effective pixel points is two is aligned with the processing when the number of effective pixel points is zero. In this case, the same processing module can be directly used without increasing additional operations, reducing the calculation complexity.

[0117] Referring to FIG. 10, an exemplary flowchart of model parameter derivation according to an embodiment of the present application is shown. In FIG. 10, assuming that the predicted image component is a chrominance component, first, adjacent reference pixel points are obtained from the selection region to form a first set of adjacent reference pixels. Then, the number of valid pixel points in the first set of adjacent reference pixels is determined. If the number of valid pixel points is greater than or equal to 4, a screening process is performed on the first set of reference pixels to obtain a second set of reference pixels. Then, the number of valid pixel points in the second set of adjacent reference pixels is determined. If the number of valid pixel points in the first set of reference pixels or the second set of reference pixels is 0, the model parameter α is set to 0, and the model parameter β is set to the default value. In this case, the predicted value corresponding to the chrominance component is filled with the default value. When the number of valid pixel points in the first set of reference pixels or the second set of reference pixels is 2, the processing steps are the same as those when the number of valid pixel points is 0. For the case where the number of valid pixel points in the second set of reference pixels is 4, first, two pixel points with relatively large chrominance components and two pixel points with relatively small chrominance components are obtained through four comparisons. Then, two average value points are obtained. Based on the two average value points, the model parameters α and β are derived, and the prediction process of the chrominance component is performed according to the constructed prediction model. Therefore, only the coding block that satisfies the number of valid pixel points in the second set of reference pixels being 4 can derive the model parameters in the CCLM mode. For the coding block with the number of valid pixel points less than 4, the method of filling with the default value can be directly used to reduce the computational complexity when the number of reference pixels in the selection region is less than or equal to 4, and furthermore, the coding and decoding performance can be basically maintained without being changed.

[0118] In the embodiments of the present application, the process of deriving model parameters is integrated. That is, for the number of valid pixel points used for deriving model parameters within the first reference pixel set, when the number of valid pixel points within the first reference pixel set is greater than or equal to the preset number, screening of valid pixel points is performed on the first reference pixel set to obtain a second reference pixel set. Then, the number of valid pixel points within the second adjacent reference pixel set is determined. When the number of valid pixel points within the second reference pixel set meets the preset number, the current coding block needs to execute the steps of deriving model parameters in the CCLM and constructing a prediction model. When the number of valid pixel points within the first reference pixel set or the second reference pixel set is less than the preset number, the current coding block can fill the predicted value corresponding to the predicted image component of the coding block using a default value. Therefore, as shown in FIG. 11, the embodiments of the present application can further provide a simplified process for deriving model parameters.

[0119] Compared with FIG. 10, the process of deriving the model parameters shown in FIG. 11 is more rationalized. In FIG. 11, assuming that the predicted image component is the chrominance component and the preset component value is 512, first, adjacent reference pixel points are obtained from the selected region to form the first set of adjacent reference pixels. Then, the number of valid pixel points in the first set of adjacent reference pixels is determined. If the number of valid pixel points in the first reference pixel set is greater than or equal to the preset number, a screening process is performed on the first reference pixel set to obtain the second reference pixel set. Then, the number of valid pixel points in the second set of adjacent reference pixels is determined. If the number of valid pixel points in the first reference pixel set or the second reference pixel set is less than the preset number, the model parameter α is set to 0 and the model parameter β is set to 512. In this case, the predicted value corresponding to the chrominance component is filled with 512. When the number of valid pixel points in the second reference pixel set meets the preset number, first, two pixel points with relatively large chrominance components and two pixel points with relatively small chrominance components are obtained through four comparisons. Then, two average value points are obtained. Based on the two average value points, the model parameters α and β are derived, and according to the constructed prediction model, the prediction process of the chrominance component is performed. Only for the coding block where the number of valid pixel points in the second reference pixel set meets the preset number, the derivation of the model parameters in the CCLM mode can be performed. For the coding block where the number of valid pixel points is less than the preset number, a method of filling with default values is directly adopted to reduce the computational complexity when the number of reference pixel points in the selected region is less than or equal to the preset number. It should be noted that the coding and decoding performance can be basically maintained without change. Usually, the preset number in the embodiments of the present application can be four.

[0120] Furthermore, in the embodiments of the present application, the number of valid pixel points in the first reference pixel set can be obtained by judging according to the number of valid pixel points in the selected region. Therefore, as shown in FIG. 12, the embodiments of the present application can further provide a simplified process for deriving another model parameter.

[0121] In FIG. 12, assuming that the predicted image component is a chrominance component and the preset component value is 512, first, a selection area is determined to obtain a first reference pixel set. Then, the number of valid pixel points in the first reference pixel set is judged. If the number of valid pixel points in the first reference pixel set is greater than or equal to the preset number, a screening process is executed on the first reference pixel set to obtain a second reference pixel set. Then, the number of valid pixel points in the second adjacent reference pixel set is judged. If the number of valid pixel points in the first reference pixel set or the second reference pixel set is less than the preset number, the predicted value corresponding to the chrominance component is filled with 512. If the number of valid pixel points in the second reference pixel set satisfies the preset number, first, two pixel points with relatively large chrominance components and two pixel points with relatively small chrominance components are obtained by four comparisons. Then, two average value points are obtained. Based on the two average value points, model parameters α and β are derived, and according to the constructed prediction model, prediction processing of the chrominance component is executed. Therefore, only for the coded block where the number of valid pixel points in the second reference pixel set satisfies the preset number, the derivation of the model parameters in the CCLM mode can be executed. For the coded block where the number of valid pixel points is less than the preset number, the method of directly filling with the default value is adopted to reduce the computational complexity when the number of reference pixels in the selection area is less than or equal to the preset number, and furthermore, the coding and decoding performance can be basically maintained without being changed. Usually, the preset number in the embodiments of the present application can be four.

[0122] JPEG0007714766000019.jpg123170

[0123] Furthermore, VVC defines the case where both the variables numSampL and numSampT are 0 (in this case, the number of valid pixel points used for deriving the model parameters to be screened can be set to 0), and directly sets the predicted value corresponding to the chrominance component to the default value. Otherwise, it is necessary to derive the model parameters, specifically as follows.

[0124] if (numSampL == 0 && numSampT == 0) Set the predicted value corresponding to the chromaticity component to the default value, else Derive the model parameters, use the constructed prediction model to predict the chromaticity component, and obtain the predicted value corresponding to the chromaticity component.

[0125] JPEG0007714766000020.jpg55170

[0126] if (numSampL + numSampT < the number of presets) Set the predicted value corresponding to the chromaticity component to the default value, else Derive the model parameters, use the constructed prediction model to predict the chromaticity component, and obtain the predicted value corresponding to the chromaticity component.

[0127] In this embodiment, by obtaining a first set of reference pixels corresponding to a predicted image component of an encoded block in a video image, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, the first set of reference pixels is screened to obtain a second set of reference pixels. The number of valid pixel points in the second set of reference pixels is less than or equal to the number of presets. When the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the second set of reference pixels is equal to the number of presets, model parameters are determined through the second set of reference pixels, and according to the model parameters, a prediction model corresponding to the predicted image component is obtained. The prediction model is used to implement a prediction process for the predicted image component to obtain a predicted value corresponding to the predicted image component. In this way, on the premise of not changing the encoding / decoding prediction performance, the derivation process of the model parameters is integrated. At the same time, when the number of valid pixel points in the adjacent reference pixel set is smaller than the number of presets, and sometimes when the number of valid pixel points is 0 or 2, no additional processing module is added, so no additional processing is required, reducing the computational complexity.

[0128] In another embodiment of the present application, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the CCLM mode can be directly disabled, and the component value of the preset can be used as a predicted value corresponding to the predicted image component. Therefore, in some embodiments, after screening the first set of reference pixels to obtain a second set of reference pixels, the method is as follows: When the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, or when the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, using the component value of the preset as a predicted value corresponding to the predicted image component, and When the number of valid pixel points in the second reference pixel set is greater than or equal to the number of presets, further including adopting the CCLM mode to implement prediction processing for the predicted image component.

[0129] Regarding the case where the number of valid pixel points in the first reference pixel set or the second reference pixel set is smaller than the number of presets, in this case, the CCLM mode can be disabled. For example, set the identifier for whether to use the CCLM mode to "disable the CCLM mode". In this case, directly fill the predicted value corresponding to the predicted image component with the default value. Only when the number of valid pixel points in the second reference pixel set is greater than or equal to the number of presets, use the CCLM mode. For example, set the identifier for whether to use the CCLM mode to "enable the CCLM mode". In this case, note that prediction processing for the predicted image component can be implemented via the CCLM mode.

[0130] Furthermore, assuming that the predicted image component is a chrominance component and the number of presets is 4, for all cases where there is a possibility of generating two valid pixel points (where the determination method for generating two valid pixel points is not particularly limited in the embodiments of the present application), further, set the model parameter α to 0 and the model parameter β to the intermediate value corresponding to the chrominance component (which can also be referred to as the default value). Thereby, the predicted values corresponding to the chrominance components of all pixel points in the coding block can be filled with the default value. For all cases where there is a possibility of generating two valid pixel points, note that both numSampL and numSampT can be set to 0, and the predicted values corresponding to the chrominance components of all pixel points in the coding block can be filled with the default value.

[0131] Furthermore, for all cases where it is possible to generate two valid pixel points, the predicted value corresponding to the chrominance component can be directly filled with the default value, or for all cases where it is possible to generate two valid pixel points, the CCLM mode can be disabled, or for all cases where it is possible to generate two or zero valid pixel points, the CCLM mode can be disabled, or for all cases where it is possible to generate two or zero valid pixel points, the predicted value corresponding to the chrominance component can be directly filled with the default value.

[0132] Therefore, when the number of valid pixel points used for deriving the model parameters is different, the unification of the derivation process of the model parameters is realized. Specifically, when the number of valid pixel points is two, no additional processing is required, and the existing processing module can be directly read (that is, the processing when the number of valid pixel points is two is adjusted to the processing when the number of valid pixel points is zero), thereby reducing the computational complexity.

[0133] The method for predicting image components in the embodiments of the present application is based on the latest reference software VTM5.0 of VVC. Under the All intra condition, for the test sequences required by JVET, according to the general test conditions, the average BD-rate changes in the Y component, Cb component, and Cr component are 0.00%, 0.02%, and 0.02% respectively, that is, the present application shows that it basically has no impact on the encoding and decoding performance.

[0134] On the premise of not affecting the encoding and decoding performance, the present application can have the following beneficial effects.

[0135] First, the present application can integrate the process of deriving model parameters in the CCLM mode. In the technical solution of the prior art, when there are two effective pixel points, an additional "duplication" operation needs to be performed to generate four available pixel points, so as to perform the same operation when there are four effective pixel points, and further, the model parameters can be derived. However, the present application can reduce the additional "duplication" operation, and at the same time, adjust the processing when the number of effective pixel points is 2 to the processing when the number of effective pixel points is 0. In each case, the same processing module can be directly used without increasing additional operations, thereby integrating the process of deriving linear model parameters.

[0136] JPEG0007714766000021.jpg93170

[0137] This embodiment provides a method for predicting an image component. Through the technical solution of this embodiment, the number of effective pixel points in the first reference pixel set is compared with a preset number. When the number of effective pixel points in the first reference pixel set or the second reference pixel set is smaller than the preset number, the preset default value is directly used as the predicted value corresponding to the predicted image component. When the number of effective pixel points in the second reference pixel set is greater than or equal to the preset number, model parameters are determined according to the first reference pixel set to construct a prediction model for the predicted image component, thereby integrating the process of deriving model parameters. Further, when the number of effective pixel points in the first reference pixel set is smaller than the preset number, no additional processing module is added, thus reducing the computational complexity.

[0138] Based on the same inventive concept as the above embodiment, referring to FIG. 13, an exemplary structural diagram of the configuration of an image component prediction apparatus 130 according to an embodiment of the present application is shown. The image component prediction apparatus 130 can include an acquisition unit 1301, a prediction unit 1302, and a screening unit 1303, where The acquisition unit 1301 is configured to acquire a first reference pixel set corresponding to a predicted image component of an encoded block in a video image. When the number of valid pixel points in the first reference pixel set is smaller than the number of presets, the prediction unit 1302 is configured to use the component value of the preset as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the first reference pixel set is greater than or equal to the number of presets, the screening unit 1303 is configured to screen the first reference pixel set to obtain a second reference pixel set, where the number of valid pixel points in the second reference pixel set is smaller than or equal to the number of presets. When the number of valid pixel points in the second reference pixel set is smaller than the number of presets, the prediction unit 1302 is further configured to use the component value of the preset as a predicted value corresponding to the predicted image component, and when the number of valid pixel points in the second reference pixel set is equal to the number of presets, to determine model parameters through the second reference pixel set, and to obtain a prediction model corresponding to the predicted image component according to the model parameters, where the prediction model is used to implement a prediction process for the predicted image component to obtain a predicted value corresponding to the predicted image component.

[0139] In the above technical solution, specifically, the acquisition unit 1301 acquires reference pixel points adjacent to at least one side of the encoded block, where the at least one side includes the left side and / or the upper side of the encoded block, and is configured to form a first reference pixel set corresponding to the predicted image component based on the reference pixel points.

[0140] In the above technical solution, specifically, the acquisition unit 1301 acquires reference pixel points in a reference row or a reference column adjacent to the encoded block. Here, the reference row is formed by rows adjacent to the upper side and the upper right side of the encoded block, and the reference column is formed by columns adjacent to the left side and the lower left side of the encoded block. Based on the reference pixel points, it is configured to form a first reference pixel set corresponding to the predicted image component.

[0141] In the above technical solution, specifically, the screening unit 1303 determines a selected pixel point position based on the pixel position and / or the image component intensity corresponding to each adjacent reference pixel point in the first reference pixel set. According to the determined selected pixel point position, it selects valid pixel points corresponding to the selected pixel point position from the first reference pixel set, and is configured to form a second reference pixel set with the selected valid pixel points. Here, the number of valid pixel points in the second reference pixel set is less than or equal to the preset number.

[0142] In the above technical solution, referring to FIG. 13, the prediction device 130 of the image component further includes a determination unit 1304 configured to determine a preset component range corresponding to the predicted image component based on the bit information of the video image, determine an intermediate value of the preset component range according to the preset component range, and use the intermediate value as the predicted value corresponding to the predicted image component. Here, the intermediate value is indicated by the preset component value.

[0143] In the above technical solution, referring to FIG. 13, the prediction device 130 of the image component further includes a filling unit 1305 configured to perform filling of predicted values for the predicted image components of each pixel point in the encoded block using the preset component values.

[0144] In the above technical solution, the prediction unit 1302 is further configured to perform a prediction process on the predicted image component of each pixel point in the encoded block based on the prediction model, so as to obtain a predicted value corresponding to the predicted image component of each pixel point.

[0145] In the above technical solution, the value of the number of presets is 4, and; the prediction unit 1302 is further configured to use the component value of the preset as the predicted value corresponding to the predicted image component when the number of valid pixel points in the first reference pixel set is 0 or 2. Correspondingly, the prediction unit 1302 is further configured to use the component value of the preset as the predicted value corresponding to the predicted image component when the number of valid pixel points in the second reference pixel set is 0 or 2.

[0146] In the above technical solution, referring to FIG. 13, the prediction device 130 of the image component further includes a determination unit 1306 configured to use the component value of the preset as the predicted value corresponding to the predicted image component when the number of valid pixel points in the first reference pixel set is smaller than the number of presets or the number of valid pixel points in the second reference pixel set is smaller than the number of presets, and to adopt the CCLM mode to perform a prediction process on the predicted image component when the number of valid pixel points in the second reference pixel set is greater than or equal to the number of presets.

[0147] In this embodiment, it should be understood that the "unit" may be a part of a circuit, a part of a processor, a part of a program or software, etc. Of course, it may be modular or non-modular. Further, each component in this embodiment may be integrated into one processing unit, each unit may physically exist separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of a software function module.

[0148] Based on the understanding that when the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, the technical solution of this embodiment can be embodied in the form of a software product in essence or in part that contributes to the prior art or the whole or part of the technical solution. Such a software product can be stored in a computer-readable storage medium, and the computer software product includes several instructions for causing a computer device (which can be a personal computer, a server, a network device, etc.) or a processor to execute all or part of the steps of the method described in this embodiment. The aforementioned storage medium includes various media that can store program codes such as a USB disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0149] Therefore, this embodiment provides a computer storage medium, and the computer storage medium stores an image component prediction program. When the image component prediction program is executed by at least one processor, it realizes the steps of the method described in any one of the above embodiments.

[0150] Based on the combination of the above-described image component prediction device 130 and the computer storage medium, referring to FIG. 14, a specific hardware structure of the image component prediction device 130 according to an embodiment of the present application is shown, which includes a network interface 1401, a memory 1402, and a processor 1403. Each component is coupled together via a bus system 1404. It can be understood that the bus system 1404 is used to implement connection communication between these components. In addition to the data bus, the bus system 1404 includes a power bus, a control bus, and a status signal bus. However, for clarity of explanation, in FIG. 14, various buses are represented as the bus system 1404. Here, the network interface 1401 is configured to transmit and receive signals in the process of transmitting and receiving information with another external network element, the memory 1402 is configured to store a computer program executable by the processor 1403, the processor 1403 is configured to execute the following steps by executing the computer program, and the steps are obtaining a first set of reference pixel corresponding to the predicted image component of the coded block in the video image, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, using the component value of the preset as the predicted value corresponding to the predicted image component; when the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, screening the first set of reference pixels to obtain a second set of reference pixels, where the number of valid pixel points in the second set of reference pixels is less than or equal to the number of presets, When the number of valid pixel points in the second reference pixel set is smaller than the number of presets, use the component values of the presets as predicted values corresponding to the predicted image components; when the number of valid pixel points in the second reference pixel set is equal to the number of presets, determine model parameters via the second reference pixel set, and obtain a prediction model corresponding to the predicted image components according to the model parameters, where the prediction model is used to implement prediction processing for the predicted image components and obtain predicted values corresponding to the predicted image components.

[0151] It should be understood that the memory 1402 in the embodiments of the present application can be a volatile memory, a non-volatile memory, or can include both volatile and non-volatile memories. Here, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example and not limitation, for example, various forms of RAM such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM) of dynamic random access memory, and direct rambus random access memory (DRRAM) can be used. It is intended that the memory 1402 of the systems and methods described herein include, but not be limited to, these and any other suitable types of memory.

[0152] The processor 1403 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 1403 or instructions in the form of software. The above processor 1403 can be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates, or transistor logic devices, discrete hardware components, etc. Each method, step, and logic block diagram disclosed in the embodiments of the present application can be realized or executed. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly executed by a hardware decoding processor, or can be executed by a combination of the hardware and software modules in the decoding processor. The software module can be arranged in a conventional storage medium such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. The storage medium is arranged in the memory 1402, and the processor 1403 reads the information in the memory 1402 and combines it with its hardware to complete the steps of the method.

[0153] It can be understood that these embodiments described in this specification can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processing (DSP), DSP devices, programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, or other electronic units configured to perform the functions described in this application, or a combination thereof.

[0154] In the case of execution using software, the techniques described in this specification can be realized through modules (processes, functions, etc.) that execute the functions described in this specification. The software code can be stored in memory and executed by a processor. The memory can be implemented inside or outside the processor.

[0155] Exemplarily, as another embodiment, the processor 1403 is further configured to execute the steps of the method according to any one of the above embodiments by executing the computer program.

[0156] Referring to FIG. 15, an exemplary structural diagram of the configuration of an encoder according to an embodiment of the present application is shown. As shown in FIG. 15, the encoder 150 can include at least the prediction device 130 for an image component according to any one of the above embodiments.

[0157] Referring to FIG. 16, there is shown an exemplary structural diagram of the configuration of the decoder according to the embodiment of the present application. As shown in FIG. 16, the decoder 160 can include at least the image component prediction device 130 described in any one of the above embodiments.

[0158] In this specification, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, item or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or also includes elements specific to these processes, methods, items or devices. It should be noted that without more limitations, an element limited by the phrase "comprising one..." does not exclude the presence of other relevant elements in the process, method, item or device comprising the element.

[0159] The numbers of the above embodiments of the present application do not represent the superiority or inferiority of the embodiments, but are for the convenience of explanation.

[0160] The methods disclosed in some method embodiments according to the present application can be arbitrarily combined without conflict to obtain new method embodiments.

[0161] The features disclosed in some product embodiments according to the present application can be arbitrarily combined without conflict to obtain new product embodiments.

[0162] The features disclosed in some method or device embodiments according to the present application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0163] The above are only specific embodiments of the present application, and the protection scope of the present application is not limited thereto. Those skilled in the art can easily conceive of changes or substitutions within the technical scope disclosed in the present application, and all of them should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Industrial Applicability

[0164] In an embodiment of the present application, by obtaining a first set of reference pixels corresponding to a predicted image component of an encoded block in a video image, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the first set of reference pixels is greater than or equal to the number of presets, the first set of reference pixels is screened to obtain a second set of reference pixels, and the number of valid pixel points in the second set of reference pixels is less than or equal to the number of presets. When the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the component value of the preset is used as a predicted value corresponding to the predicted image component. When the number of valid pixel points in the second set of reference pixels is equal to the number of presets, model parameters are determined through the second set of reference pixels, and according to the model parameters, a prediction model corresponding to the predicted image component is obtained. The prediction model is used to implement a prediction process for the predicted image component to obtain a predicted value corresponding to the predicted image component. In this way, when the number of valid pixel points in the first set of reference pixels is smaller than the number of presets, or when the number of valid pixel points in the second set of reference pixels is smaller than the number of presets, the default value of the preset is directly used as the predicted value corresponding to the predicted image component. Only when the number of valid pixel points in the second set of reference pixels meets the number of presets, model parameters are determined according to the first set of reference pixels to establish a prediction model for the predicted image component, thereby integrating the derivation process of the model parameters. Furthermore, when the number of valid pixel points in the first set of reference pixels or the second set of reference pixels is smaller than the number of presets, especially when the number of valid pixel points is 0 or 2, since no additional processing module is added, the default value of the preset is directly used as the predicted value corresponding to the predicted image component, no additional processing is required, and the computational complexity is reduced.

Claims

1. A method for predicting an image component, applicable to a decoder, comprising: obtaining a first reference sample set corresponding to a predicted image component of a decoded block in a video image; when the number of valid sample points in the first reference sample set is equal to 0, using a preset component value as a predicted value corresponding to the predicted image component; when the number of valid sample points in the first reference sample set is greater than or equal to 4, processing the first reference sample set to obtain a second reference sample set, wherein the number of valid sample points in the second reference sample set is less than or equal to a preset number, and the preset number is 4; when the number of valid sample points in the second reference sample set is equal to the preset number, determining model parameters based on the second reference sample set; The method for predicting the image component further comprises: obtaining a prediction model corresponding to the predicted image component according to the model parameters; performing a prediction process on the predicted image component based on the prediction model to obtain a predicted value corresponding to the predicted image component; Obtaining a first reference sample set corresponding to a predicted image component of a decoded block in the video image comprises: obtaining reference sample points adjacent to at least one side of the decoded block, wherein the at least one side includes the left side and / or the upper side of the decoded block; determining a first reference sample set corresponding to the predicted image component based on the reference sample points. The method for predicting the image component is as described above.

2. Obtaining a first reference sample set corresponding to a predicted image component of a decoded block in the video image comprises: obtaining reference sample points in a reference row and / or a reference column adjacent to the decoded block, wherein the reference row is formed by rows adjacent to the upper side and the upper right side of the decoded block, and the reference column is formed by columns adjacent to the left side and the lower left side of the decoded block; determining a first reference sample set corresponding to the predicted image component based on the reference sample points. The method for predicting image components according to claim 1 .

3. processing the first reference sample set includes screening the first reference sample set; Screening the first reference sample set to obtain a second reference sample set includes: determining selected sample point locations based on sample locations corresponding to adjacent reference sample points in the first reference sample set; According to the determined selected sample point positions, selecting valid sample points corresponding to the selected sample point positions from the first reference sample set, and determining a second reference sample set based on the valid sample points obtained by the selection, wherein the number of valid sample points in the second reference sample set is less than or equal to a preset number; The method for predicting an image component according to claim 1 or 2.

4. Using the preset component values as predicted values corresponding to the predicted image components includes: determining the preset component values based on a bit depth of the video image; The preset component value is 1<<(BitDepth-1), where BitDepth is the bit depth of the predicted image component. The method for predicting image components according to claim 1 .

5. The method for predicting image components comprises: and performing, for each sample point in the decoding block, a fill-in of a predicted value for a predicted image component of each sample point using the preset component value. The method for predicting image components according to claim 1 .

6. After obtaining a prediction model corresponding to the image component to be predicted according to the model parameters, the method for predicting the image component comprises: and performing a prediction process on a predicted image component of each sample point in the decoded block based on the prediction model to obtain a predicted value corresponding to the predicted image component of each sample point. The method for predicting image components according to claim 1 .

7. 1. A method for predicting image components applied to an encoder, comprising: obtaining a first reference sample set corresponding to a predicted image component of a coding block in a video image; if the number of valid sample points in the first reference sample set is equal to 0, using a preset component value as a predicted value corresponding to the predicted image component; When the number of valid sample points in the first reference sample set is greater than or equal to 4, processing the first reference sample set to obtain a second reference sample set, wherein the number of valid sample points in the second reference sample set is less than or equal to a preset number, and the preset number is 4, and when the number of valid sample points in the second reference sample set is equal to the preset number, determining model parameters based on the second reference sample set, The method for predicting the image component further includes obtaining a prediction model corresponding to the predicted image component according to the model parameters, performing a prediction process on the predicted image component based on the prediction model to obtain a predicted value corresponding to the predicted image component, Obtaining a first reference sample set corresponding to the predicted image component of the encoded block in the video image includes obtaining reference sample points in a reference row and / or a reference column adjacent to the encoded block, wherein the reference row is formed by rows adjacent to the upper side and the upper right side of the encoded block, and the reference column is formed by columns adjacent to the left side and the lower left side of the encoded block, determining a first reference sample set corresponding to the predicted image component based on the reference sample points. The method for predicting the image component includes the above. Claim 8 Processing the first reference sample set includes screening the first reference sample set. Screening the first reference sample set to obtain a second reference sample set includes determining a selected sample point position based on the sample positions corresponding to adjacent reference sample points in the first reference sample set, selecting valid sample points corresponding to the selected sample point position from the first reference sample set according to the determined selected sample point position, and determining a second reference sample set based on the selected valid sample points. The number of valid sample points in the second reference sample set is less than or equal to a preset number. The method for predicting an image component according to claim 7. Claim 9 Using the preset component values as predicted values corresponding to the predicted image components includes: determining the preset component values based on a bit depth of the video image; The preset component value is 1<<(BitDepth-1), where BitDepth is the bit depth of the predicted image component. The method for predicting image components according to claim 7.

10. The method for predicting image components comprises: and performing, for each sample point in the coding block, a fill-in of a predicted value for a predicted image component of each sample point using the preset component value. The method for predicting image components according to claim 7.

11. After obtaining a prediction model corresponding to the image component to be predicted according to the model parameters, the method for predicting the image component comprises: and performing a prediction process on a predicted image component of each sample point in the coding block based on the prediction model to obtain a predicted value corresponding to the predicted image component of each sample point. The method for predicting image components according to claim 7.

12. 1. A decoder comprising a memory and a processor, the memory is configured to store a computer program executable by the processor; The decoder, wherein the processor, when executing the computer program, is configured to perform the method for predicting an image component according to any one of claims 1 to 6.

13. 1. An encoder comprising a memory and a processor, the memory is configured to store a computer program executable by the processor; The encoder, wherein the processor, when executing the computer program, is configured to perform the method for predicting an image component according to any one of claims 7 to 11.

Images