Video image component prediction method and apparatus, computer storage medium

Abstract

This invention provides a method and apparatus for predicting video image components, and a computer storage medium. [Solution] The video image component prediction method obtains a set of reference values ​​for the first image component of the current block, determines multiple first image component reference values ​​from the set of reference values ​​for the first image component, performs a first filtering process on the sample values ​​of the pixel points corresponding to the multiple first image component reference values ​​to obtain multiple filtered first image reference sample values, determines the image component reference values ​​awaiting prediction corresponding to the multiple filtered first image reference sample values, determines the parameters of the component linear model based on the multiple filtered first image reference sample values ​​and the image component reference values ​​awaiting prediction, performs a mapping process on the reconstructed value of the first image component of the current block based on the component linear model to obtain the mapping value, and determines the predicted value of the image component awaiting prediction of the current block based on the mapping value.

Description

[Technical Field]

[0001] The embodiments of this application relate to the technical field of video encoding and decoding, and more particularly to a method and apparatus for predicting video image components, and a computer storage medium. [Background technology]

[0002] With the increasing demand from people for higher video display quality, new video applications such as high-resolution and ultra-high-resolution video have emerged. As these high-resolution, high-quality video viewing applications expand, the requirements for video compression technology are also increasing. H.265 / High Efficiency Video Coding (HEVC) is the current latest international video compression standard, and while it is about 50% better than the previous generation video coding standard H.264 / Advanced Video Coding (AVC), it still cannot meet the rapidly developing needs of video applications, and in particular, it cannot meet the needs of new video applications such as ultra-high-definition video and virtual reality (VR).

[0003] The coding tools used in the next-generation video coding standard for Versatile Video Coding (VVC) employ a linear model-based prediction method, where the color difference prediction value of the color difference component can be obtained from the luminance component reconstructed via the linear model.

[0004] However, when predicting video components using a linear model, after performing downsampling using pixel values ​​in the luminance adjacent region, it is necessary to find the maximum and minimum values ​​from the reference sample points obtained by downsampling in order to build the linear model. Due to the large number of adjacent reference blocks, the complexity of building the model in the manner described above is high, resulting in low efficiency in color difference prediction, which in turn affects the efficiency of video encoding and decoding. [Overview of the project]

[0005] The embodiments of this application provide a video image component prediction method and apparatus, as well as a computer storage medium, which can improve video encoding and decoding efficiency by reducing the complexity of video component prediction and improving prediction efficiency.

[0006] The technical solution of the embodiment of this application can be realized as follows.

[0007] Embodiments of the present application provide a video component prediction method, and the method is Obtain the set of reference values ​​for the first image component of the current block, Determining multiple first image component reference values ​​from the set of reference values ​​for the first image component, The first filtering process is performed on the sample values ​​of the pixel points corresponding to the plurality of first image component reference values ​​to obtain a plurality of filtered first image reference sample values. The process involves determining the predicted image component reference values ​​corresponding to the multiple filtered first image reference sample values, wherein the predicted image component is a different image component from the first image component. The parameters of the component linear model are determined based on the plurality of filtered first image reference sample values ​​and the predicted image component reference values, wherein the component linear model represents a linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the predicted image component. Based on the component linear model, a mapping process is performed on the reconstructed value of the first image component of the current block to obtain the mapping value. This includes determining the predicted value of the pending image component of the current block based on the mapping value.

[0008] Embodiments of the present application provide a video component prediction device, the device is An acquisition unit configured to acquire a set of reference values ​​for the first image component of the current block, and a determination unit configured to determine a plurality of first image component reference values ​​from the set of reference values ​​for the first image component, A filtering unit is configured to perform a first filtering process on each of the sample values ​​of pixel points corresponding to the plurality of first image component reference values ​​to obtain a plurality of filtered first image reference sample values. The system includes a prediction unit configured to determine the predicted value of the image component awaiting prediction for the current block based on the mapping value, The determination unit further determines a pending image component reference value corresponding to the plurality of filtered first image reference sample values, wherein the pending image component is a different image component from the first image component, and is configured to determine the parameters of the component linear model based on the plurality of filtered first image reference sample values ​​and the pending image component reference value, wherein the component linear model represents a linear mapping relationship that maps the sample value of the first image component to the sample value of the pending image component. The filtering unit is further configured to perform a mapping process on the reconstructed value of the first image component of the current block based on the component linear model, and to obtain a mapping value.

[0009] Embodiments of the present application provide a video component prediction device, the device is A memory configured to store executable video component prediction instructions, The invention comprises a processor configured to implement the video component prediction method according to the embodiment of the present invention when executing an executable video component prediction instruction stored in the memory.

[0010] Embodiments of the present application provide a computer-readable storage medium including executable video component prediction instructions, which are configured to implement a video component prediction method according to embodiments of the present application when executed by a processor.

[0011] In an embodiment of the present invention, a video image component prediction method is provided, in which the video image component prediction device first selects a plurality of first image component reference values ​​based on a set of reference values ​​of first image components corresponding to the currently acquired block, then performs a filtering process based on the pixel point positions of the selected plurality of first image component reference values ​​to obtain a plurality of filtered first image reference sample values, then finds the awaited image component reference values ​​corresponding to the plurality of filtered first image reference sample values ​​to obtain component linear model parameters, constructs a component linear model based on the parameters of the component linear model, and then uses the constructed component linear model to perform a prediction process for awaited image components. In the component linear model construction process, by first selecting a plurality of first image component reference values ​​and then performing a filtering process based on the positions corresponding to the selected plurality of first image component reference values ​​to construct a component linear model, the workload of filtering the pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, thereby reducing the complexity of constructing the component linear model, further reducing the complexity of video component prediction, improving prediction efficiency, and improving video encoding and decoding efficiency. [Brief explanation of the drawing]

[0012] [Figure 1] This is a schematic diagram showing the relationship between the current block and adjacent reference pixel points according to an embodiment of the present application. [Figure 2] This is an architectural diagram of a video image component prediction system according to an embodiment of the present invention. [Figure 3A] This is a schematic block diagram of a video encoding system according to an embodiment of the present invention. [Figure 3B] This is a schematic block diagram of a video decoding system according to an embodiment of the present invention. [Figure 4] This is flowchart 1 of the video image component prediction method in the embodiment of the present invention. [Figure 5] This is flowchart 2 of the video image component prediction method in the embodiment of the present invention. [Figure 6]It is the flowchart 3 of the video image component prediction method in the embodiment of the present application. [Figure 7] It is the structural diagram for constructing a prediction model based on the maximum value and the minimum value according to the embodiment of the present application. [Figure 8] It is the schematic structural diagram 1 of the video image component prediction device according to the embodiment of the present application. [Figure 9] It is the schematic structural diagram 2 of the video image component prediction device according to the embodiment of the present application.

Embodiments for Carrying out the Invention

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

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

[0015] Hereinafter, concepts such as intra-frame prediction, video encoding / decoding, etc. will be described first.

[0016] The main function of predictive encoding / decoding is to generate a predicted value of the current block using an existing reconstructed image in space or time in video encoding / decoding, and achieve the purpose of reducing the amount of transmitted data by only transmitting the difference value between the original value and the predicted value.

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

[0018] In the embodiments of this application, a video image is typically represented using three image components to represent a processing block. These three image components are the luminance component, the blue difference component, and the red difference component, respectively. Specifically, the luminance component is typically denoted by the code Y, the blue difference component by the code Cb, and the red difference component by the code Cr.

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

[0020] 4:4:4 format: This means that the blue difference component or the red difference component is not downsampled. This means that for every four consecutive pixel points on each scanline, four samples of the luminance component, four samples of the blue difference component, and four samples of the red difference component are extracted.

[0021] 4:2:2 format: This means that the luminance component is sampled horizontally at a 2:1 ratio with respect to the blue difference component or red difference component, and no downsampling is performed vertically. This means that for every four consecutive pixel points on each scanline, four samples of the luminance component, two samples of the blue difference component, and two samples of the red difference component are extracted.

[0022] 4:2:0 format: This means that the luminance component is sampled horizontally at a 2:1 ratio relative to the blue difference component or red difference component, and downsampled vertically at a 2:1 ratio. This means that for every two consecutive pixel points on the horizontal and vertical scanlines, two samples of the luminance component, one sample of the blue difference component, and one sample of the red difference component are extracted.

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

[0024] Based on the above concept, embodiments of the present application provide a network architecture for a video coding and decoding system that includes a video image component prediction method for in-frame prediction. Figure 2 is a structural diagram of the network architecture for video coding and decoding according to embodiments of the present application. As shown in Figure 2, the network architecture includes one or more electronic devices 11 to 1N and a communication network 01, where the electronic devices 11 to 1N can perform video interactions via the communication network 01. In the implementation process, the electronic devices can be various types of devices equipped with video coding and decoding capabilities. For example, the electronic devices can include, but are not limited to, mobile phones, tablet computers, personal computers, personal digital assistants, navigators, digital telephones, video phones, televisions, sensing devices, servers, etc. Here, the in-frame prediction device in embodiments of the present application can be one of the above-mentioned electronic devices.

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

[0026] Illustratively, referring to Figure 3A, the configuration of the video encoder 21 includes a transform and quantization unit 211, an in-frame estimation unit 212, an in-frame prediction unit 213, a motion compensation unit 214, a motion estimation unit 215, an inverse transform and inverse quantization unit 216, a filter control analysis unit 217, a filtering unit 218, an entropy coding unit 219, and a decoded image cache unit 210, among others. Here, the filtering unit 218 can implement deblocking filtering and sample adaptive offset (SAO) filtering, and the entropy coding unit 219 can implement header information coding and context-based adaptive binary arithmetic coding (CABAC). For input source video data, a coding tree unit (CTU) can be used to obtain a pending coding block of a current video frame. The residual information obtained by performing intra-frame or inter-frame prediction on the pending coding block is then transformed by the transformation and quantization unit 211 (including transforming the residual information from the pixel domain to the transformation domain, quantizing the resulting transformation coefficients, and thereby further reducing the bitrate). The intra-frame estimation unit 212 and the intra-frame prediction unit 213 are configured to perform intra-frame prediction on the pending coding block, for example, determining the intra-frame prediction mode used to encode the pending coding block. The motion compensation unit 214 and the motion estimation unit 215 are configured to perform inter-frame prediction coding of the pending coding block for one or more blocks in one or more reference frames and provide time prediction information. Here, the motion estimation unit 215 is configured to estimate a motion vector, so that the motion of the pending coding block can be estimated based on the motion vector, and then the motion compensation unit 214 performs motion compensation based on the motion vector. After determining the in-frame prediction mode, the in-frame prediction unit 213 is configured to further provide the selected in-frame prediction data to the entropy coding unit 219, and the motion estimation unit 215 also transmits the computationally determined motion vector data to the entropy coding unit 219. Furthermore, the inverse transform and inverse quantization unit 216 is configured to reconstruct the pending coding block, i.e., to reconstruct the residual block in the pixel domain, and artifacts due to blocking effects in the reconstructed residual block are removed by the filter control analysis unit 217 and the filtering unit 218. The reconstructed residual block is then added to one of the prediction blocks in the frame of the decoded image cache unit 210 to generate a reconstructed video coding block.The entropy coding unit 219 is configured to encode various coding parameters and quantized transformation coefficients, and in a CABAC-based coding algorithm, the context content can be based on adjacent coding blocks and can be used to encode information indicating the determined in-frame prediction mode to output a bitstream of the video data. The decoded image cache unit 210 is configured to store reconstructed video coding blocks used for prediction reference. As video is coded, new reconstructed video coding blocks are continuously generated, and these reconstructed video coding blocks are stored in the decoded image cache unit 210.

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

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

[0029] In the next-generation video coding standard H.266, cross-component prediction (CCP) has been extended and improved to further enhance coding and decoding performance and efficiency, and cross-component linear model prediction (CCLM) has been proposed. In H.266, CCLM enables prediction from the luminance component to the blue difference component, from the luminance component to the red difference component, and between the blue difference component and the red difference component. The following describes video component prediction methods based on conventional CCLM.

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

[0031] Figure 4 is a flowchart illustrating the implementation of a video image component prediction method in an embodiment of the present invention, and as shown in Figure 4, the method includes the following steps.

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

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

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

[0035] In step S104, a prediction await image component reference value corresponding to the first image reference sample value after multiple filtering is determined, where the prediction await image component is an image component different from the first image component.

[0036] In step S105, the parameters of the component linear model are determined based on the first image reference sample values ​​and the predicted image component reference values ​​after multiple filtering, where the component linear model represents a linear mapping relationship that maps the sample values ​​of the first image component to the sample values ​​of the predicted image component.

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

[0038] In step S107, the predicted values ​​of the image components awaiting prediction for the current block are determined based on the mapping values.

[0039] In step S101, in the embodiments of the present application, the current block is an encoded or decoded block awaiting image component prediction. In the embodiments of the present application, the video image component prediction device obtains a first image component reference value of the current block, where the first image component reference value set includes one or more first image component reference values. The reference value of the current block can be obtained from a reference block, which may be an adjacent block to the current block or a non-adjacent block to the current block, but the embodiments of the present application are not limited thereto.

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

[0041] In the embodiments of the present application, an adjacent processing block corresponding to the current block is a processing block adjacent to one or more sides of the current block, and one or more adjacent sides may refer to the upper side adjacent to the current block, or the left side adjacent to the current block, or both the upper and left sides adjacent to the current block, but the embodiments of the present application are not limited thereto.

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

[0043] In the embodiments of this application, one or more reference pixel points may be adjacent pixel points or non-adjacent pixel points, but the embodiments of this application are not limited thereto. In this application, adjacent pixel points are used as an example.

[0044] Here, one or more adjacent pixel points on one or more edges corresponding to the adjacent processing block of the current block are used as one or more adjacent reference pixel points corresponding to the current block, and each adjacent reference pixel point corresponds to three image component reference values ​​(i.e., a first image component reference value, a second image component reference value, and a third image component reference value). Therefore, the video image component prediction device can obtain the reference values ​​of the first image component at each adjacent reference pixel point of the one or more adjacent reference pixel points corresponding to the current block as a set of reference values ​​for the first image component, and in this way obtain one or more first image component reference values. That is, one or more first image component reference values ​​represent the reference values ​​of the corresponding first image component at one or more adjacent pixel points in the adjacent reference block corresponding to the current block. Here, the first image component in the embodiment of the present application is used to predict other image components.

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

[0046] The first image component is the luminance component, and the predicted image component is either the first or second chrominance component, or The first image component is the first chrominance component, and the image component awaiting prediction is either the luminance component or the second chrominance component, or The first image component is the second chrominance component, and the image component awaiting prediction is either the luminance component or the first chrominance component, or The first image component is the first color component, and the image component awaiting prediction is either the second or third color component, or... The first image component is the second color component, and the image component awaiting prediction is either the first or third color component, or... The first image component is the third color component, while the image component awaiting prediction is either the second or first color component.

[0047] In some of the embodiments of this application, the first color component is the red component, the second color component is the green component, and the third color component is the blue component.

[0048] Here, the first color difference component may be the blue color difference component and the second color difference component may be the red color difference component, or the first color difference component may be the red color difference component and the second color difference component may be the blue color difference component. Here, the first color difference component and the second color difference component only need to represent the blue color difference component and the red color difference component, respectively.

[0049] Let's illustrate this by using the example that the first chromatic difference component can be the blue chromatic difference component and the second chromatic difference component can be the red chromatic difference component. If the first image component is the luminance component and the image component awaiting prediction is the first chromatic difference component, the video image component prediction device can predict the blue chromatic difference component using the luminance component. If the first image component is the luminance component and the image component awaiting prediction is the second chromatic difference component, the video image component prediction device can predict the red chromatic difference component using the luminance component. If the first image component is the first chromatic difference component and the image component awaiting prediction is the second chromatic difference component, the video image component prediction device can predict the red chromatic difference component using the blue chromatic difference component. If the first image component is the second chromatic difference component and the image component awaiting prediction is the first chromatic difference component, the video image component prediction device can predict the blue chromatic difference component using the red chromatic difference component.

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

[0051] In some embodiments of the present invention, the video image component prediction device can determine the maximum and minimum first image component reference values ​​by comparing one or more first image component reference values ​​included in a set of first image component reference values.

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

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

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

[0055] In Method 1, the first image component reference values ​​of one or more first image component reference values ​​are sequentially compared to determine the largest first image component reference value and the smallest first image component reference value.

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

[0057] In other words, in the embodiments of the present invention, the video image component prediction device selects the first image component reference value with the largest value from one or more first image component reference values ​​as the largest first image component reference value, and selects the one with the smallest value as the smallest first image component reference value. The determination method may be to compare two at a time sequentially, or to determine after sorting, but the embodiments of the present invention do not limit the specific determination method.

[0058] The video image component prediction device may also select at least two first image component reference values ​​from pixel point positions corresponding to one or more first image component reference values, where several first image component reference values ​​correspond to preset positions (preset pixel point positions). Then, it divides based on the at least two first image component reference values ​​to obtain a maximum dataset (maximum image component reference value set) and a minimum dataset (minimum image component reference value set), and determines the maximum and minimum first image component reference values ​​based on the maximum and minimum datasets. Here, the process of determining the maximum and minimum first image component reference values ​​based on the maximum and minimum datasets may involve performing an averaging process on the maximum dataset to obtain the maximum first image component reference value, and performing an averaging process on the minimum dataset to obtain the minimum first image component reference value. Other methods may also be used to determine the maximum and minimum values, but the embodiments of this application are not limited thereto.

[0059] The number of numerical values ​​in the largest and smallest datasets are integers greater than or equal to 1, and the number of numerical values ​​in the two sets may be the same or different, but the embodiments of this application are not limited to this.

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

[0061] For example, a video image component prediction device can define a maximum image component reference value set as the M (where M is greater than or equal to 4) largest first sub-image component reference values ​​out of at least two first sub-image component reference values, define a minimum image component reference value set as the two first sub-image component reference values ​​excluding the M largest first sub-image component reference values, and finally, perform an averaging process on the maximum image component reference value set to obtain the largest first image component reference value, and perform an averaging process on the minimum image component reference value set to obtain the smallest first image component reference value.

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

[0063] The embodiments of this application do not limit the method by which the video image component prediction device determines the maximum and minimum first image component reference values. For example, the video image component prediction device may divide one or more first image component reference values ​​into three or four sets according to their size, process each set to obtain one representative parameter, and then select the maximum and minimum parameters from the representative parameter as the maximum and minimum first image component reference values.

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

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

[0066] The assignment of preset positions in rows and columns may be uniform or in a preset manner, but the embodiments of this application are not limited thereto. For example, if the number of preset positions is 4, and adjacent rows and adjacent columns correspond to one or more first image component reference values, then two first image component reference values ​​can be selected from the first image component reference values ​​corresponding to adjacent rows, and two first image component reference values ​​can be selected from the first image component reference values ​​corresponding to adjacent columns. Alternatively, one first image component reference value can be selected from the first image component reference values ​​corresponding to adjacent rows, and three first image component reference values ​​can be selected from the first image component reference values ​​corresponding to adjacent columns, but the embodiments of this application are not limited thereto.

[0067] A video image component prediction device can determine the maximum and minimum values ​​among one or more first image component reference values. That is, it obtains the maximum value (maximum first image component reference value) and the minimum value (minimum first image component reference value) among one or more first image component reference values. Alternatively, it can determine multiple reference values ​​from preset positions of one or more first image component reference values, and then obtain the maximum and minimum first image component reference values ​​through processing. Here, in order to match or approximate the sampling positions of other video components, it is necessary to perform filtering based on the pixel point positions corresponding to the maximum and minimum first image component reference values ​​before performing subsequent processing.

[0068] In step S103, the video image component prediction device performs a first filtering process on each of the sample values ​​of pixel points corresponding to the multiple first image component reference values ​​that have been determined, and obtains multiple filtered first image reference sample values.

[0069] In the embodiments of the present application, the plurality of filtered first image reference sample values ​​may be the largest filtered first image component reference value and the smallest filtered first image component reference value, or a plurality of reference sample values ​​including the largest filtered first image component reference value and the smallest filtered first image component reference value, or any other plurality of reference sample values, but the embodiments of the present application are not limited thereto.

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

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

[0072] Furthermore, since the determined multiple first image component reference values ​​can be the maximum first image component reference value and the minimum first image component reference value, the filtering process performs a filtering process (i.e., a first filtering process) on the pixel point positions (i.e., the sample values ​​of the corresponding pixel points) for determining the maximum first image component reference value and the minimum first image component reference value. By doing so, the corresponding filtered maximum first image component reference value and filtered minimum image component reference value (i.e., multiple filtered first image reference sample values) can be obtained. In this way, a component linear model can subsequently be constructed based on the filtered maximum first image component reference value and the filtered minimum first image component reference value.

[0073] In the embodiments of the present application, the filtering method may be, but is not limited to, methods such as upsampling, downsampling, and low-pass filtering. Here, the downsampling method may include, but is not limited to, methods such as averaging, interpolation, or median.

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

[0075] For example, a video image component prediction device can perform downsampling filtering on pixel point positions to determine the maximum and minimum first image component reference values, thereby obtaining the corresponding filtered maximum and minimum first image component reference values.

[0076] The following explanation uses downsampling as an averaging method.

[0077] The video component prediction device performs an average calculation of the first image component for a region composed of the position corresponding to the largest first image component reference value and its adjacent pixel point positions, merges the pixels of this block region into a single pixel, and the average result is the first image component reference value corresponding to the merged pixel point, i.e., the largest first image component reference value after filtering. Similarly, the video component prediction device performs an average calculation of the first image component for a region composed of the position corresponding to the smallest first image component reference value and its adjacent pixel point positions, merges the pixels of this block region into a single pixel, and the average result is the first image component reference value corresponding to the merged pixel point, i.e., the smallest first image component reference value after filtering.

[0078] In the embodiments of this application, the downsampling process of the video image component prediction device is implemented by a filter, and specifically, the position range of vector pixel points adjacent to the position corresponding to the largest first image component reference value can be determined by the type of filter, but the embodiments of this application are not limited thereto.

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

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

[0081] In some embodiments of the present invention, the video image component prediction device determines the maximum pending image component reference value corresponding to the maximum first image component reference value after filtering, and the minimum pending image component reference value corresponding to the minimum first image component reference value after filtering.

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

[0083] In the embodiment of the present invention, the video image component prediction device performs downsampling (i.e., filtering) and achieves alignment with the position of the image awaiting prediction. In this way, the reference value of the image component awaiting prediction that corresponds to the first image component reference sample value after filtering can be determined. For example, the maximum reference value of the image component awaiting prediction that corresponds to the maximum first image component reference value after filtering, and the minimum reference value of the image component awaiting prediction that corresponds to the minimum first image component reference value after filtering can be determined. In this way, the video image component prediction device has determined two points, (maximum first image component reference value after filtering, maximum image component reference value awaiting prediction) and (minimum first image component reference value after filtering, minimum image component reference value awaiting prediction), and can derive model parameters and construct a component linear model according to the principle of "determining a line with two points".

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

[0085] In some embodiments of the present invention, the implementation method by which the video image component prediction device determines the parameters of the component linear model based on the maximum first image component reference value after filtering, the maximum pending image component reference value, the minimum first image component reference value after filtering, and the minimum pending image component reference value may include the following methods. In method (1), the parameters of the component linear model include a multiplier factor and an additive offset. Therefore, the video image component prediction device can calculate a first difference value between the maximum pending image component reference value and the minimum pending image component reference value, calculate a second difference value between the maximum first image component reference value and the minimum first image component reference value, set the multiplier factor as the ratio of the first difference value and the second difference value, calculate a first product between the maximum first image component reference value and the multiplier factor, and set the additive offset as the difference value between the maximum pending image component reference value and the first product, or calculate a second product between the minimum first image component reference value and the multiplier factor and set the additive offset as the difference value between the minimum pending image component reference value and the second product. In method (2), a first subcomponent linear model is constructed using the maximum first image component reference value after filtering, the maximum pending image component reference value, and a preset initial linear model. A second subcomponent linear model is constructed using the minimum first image component reference value after filtering, the minimum pending image component reference value, and a preset initial linear model. Model parameters are obtained based on the first and second subcomponent linear models, and a component linear model is constructed using the model parameters and the preset initial linear model.

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

[0087] For example, since the component linear model represents a linear mapping relationship between the first image component and the image component awaiting prediction, the video image component prediction device can predict the image component awaiting prediction based on the first image component and the component linear model, and the image component awaiting prediction in the embodiment of the present application may be a color difference component.

[0088] For example, a component linear model may be as shown in equation (1) below.

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

[0090] The specific implementation of the model parameters will be explained in detail in subsequent examples.

[0091] The video image component prediction device selects the maximum and minimum first image component reference values ​​based on one or more directly acquired first image component reference values ​​corresponding to the current block, and then constructs a component linear model by performing downsampling based on the positions corresponding to the selected maximum and minimum first image component reference values. In this way, the workload of downsampling the pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, which in turn reduces the complexity of constructing the component linear model, further reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

[0092] In steps S106 and S107, in the embodiments of the present invention, the video image component prediction device can, after acquiring a component linear model, directly perform video component prediction on the current block using the component linear model, thereby obtaining predicted values ​​for the image components awaiting prediction. Here, the video image component prediction device can perform a mapping process on the reconstructed value of the first image component of the current block based on the component linear model to obtain a mapping value, and then determine the predicted values ​​for the image components awaiting prediction of the current block based on the mapping value.

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

[0094] In some embodiments of the present invention, the video image component prediction device sets the mapping value as the predicted value of the image component awaiting prediction for the current block.

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

[0096] In some embodiments of the present invention, the video image component prediction device may also perform a third filtering process on the mapping values ​​to obtain a third filtered value of the mapping values ​​and set the third filtered value as the predicted value of the image component awaiting prediction for the current block.

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

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

[0099] In the component linear model construction process, multiple first image component reference values ​​are selected, and then filtering is performed based on the positions corresponding to the selected multiple first image component reference values ​​to construct the component linear model. This reduces the workload of filtering pixel points corresponding to the current block, i.e., the filtering operation is reduced, which in turn reduces the complexity of constructing the component linear model, further reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

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

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

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

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

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

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

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

[0107] In step S207, the predicted values ​​of the image components awaiting prediction for the current block are determined based on the mapping values.

[0108] In the embodiments of this application, the processes of steps S201 to S207 have already been described in the above embodiments and will not be described again here.

[0109] Furthermore, when the video image component prediction device performs prediction, it first applies first image component filtering to the current block to obtain the first image component reconstruction value corresponding to the current block, and then obtains the predicted value of the image component awaiting prediction for the current block based on the component linear model and the first image component reconstruction value.

[0110] In the embodiments of the present invention, after the video image component prediction device acquires a component linear model, the smallest unit for predicting the current block is a pixel point. Therefore, it is necessary to predict the predicted value of the image component awaiting prediction corresponding to each pixel point in the current block using the first image component reconstruction value corresponding to that pixel point. Here, the video image component prediction device first performs first image component filtering (e.g., downsampling) on ​​the current block to obtain the first image component reconstruction value corresponding to the current block, specifically obtaining the first image component reconstruction value for each pixel point corresponding to the current block.

[0111] In the embodiments of the present invention, the first image component reconstruction value represents the reconstruction value of the first image component corresponding to one or more pixel points of the current block.

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

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

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

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

[0116] In the embodiments of the present invention, the video image component prediction device constructs a component linear model based on the maximum image component reference value and the minimum image component reference value after filtering. In this process, based on the principle of "determining a line with two points," if the first image component is a horizontal coordinate and the image component awaiting prediction is a vertical coordinate, the values ​​of the horizontal coordinates of the two points are known, and it is necessary to determine the values ​​of the vertical coordinates corresponding to those two points before determining a linear model, i.e., a component linear model, according to the principle of "determining a line with two points."

[0117] In some embodiments of the present invention, the video image component prediction device converts the sampling point (Sample) position of the first image component reference value corresponding to the largest first image component reference value to the position of the first sampling point, sets the largest pending prediction image component reference value as the reference value at the position of the first sampling point among the pending prediction image component reference values, converts the sampling point position of the first image component reference value corresponding to the smallest first image component reference value to the position of the second sampling point, and sets the smallest pending prediction image component reference value as the reference value at the position of the second sampling point among the pending prediction image component reference values.

[0118] For illustrative purposes, let us explain using the example that the reference pixel point is an adjacent pixel point. Based on the above description of adjacent blocks, the video image component prediction device can obtain one or more predicted image component reference values ​​corresponding to the current block, where one or more predicted image component reference values ​​may refer to the reference values ​​of predicted image components at each adjacent reference pixel point of the one or more reference pixel points corresponding to the current block, and use this as a single predicted image component reference value. In this way, the video image component prediction device obtains one or more predicted image component reference values.

[0119] The video image component prediction device finds the first adjacent reference pixel point where the largest first image component reference value after filtering is located, from the pixel points corresponding to one or more pending image component reference values, and uses the pending image component reference value corresponding to the first adjacent reference pixel point as the largest pending image component reference value, that is, it determines the largest pending image component reference value corresponding to the largest first image component reference value after filtering. It then finds the second adjacent reference pixel point where the smallest first image component reference value after filtering is located, from the pixel points corresponding to one or more pending image component reference values, and uses the pending image component reference value corresponding to the second adjacent reference pixel point as the smallest pending image component reference value, that is, it determines the smallest pending image component reference value corresponding to the smallest first image component reference value after filtering. Finally, following the principle of "determining a line with two points," it determines a straight line based on the two points (largest first image component reference value after filtering, largest pending image component reference value) and (smallest first image component reference value after filtering, smallest pending image component reference value), and the function (mapping relationship) represented by this straight line is a component linear model.

[0120] In some embodiments of the present invention, the video image component prediction device also first performs filtering on adjacent pixel point locations to obtain one or more predicted image component reference values ​​for the filtered pixel points, then finds the first adjacent reference pixel point where the largest first image component reference value after filtering is located from the filtered pixel point locations, uses the predicted image component reference value (one of the one or more predicted image component reference values) corresponding to the first adjacent reference pixel point as the largest predicted image component reference value, that is, determines the largest predicted image component reference value corresponding to the largest first image component reference value after filtering, finds the second adjacent reference pixel point where the smallest first image component reference value after filtering is located from the filtered pixel point locations, uses the predicted image component reference value corresponding to the second adjacent reference pixel point as the smallest predicted image component reference value, that is, determines the smallest predicted image component reference value corresponding to the smallest first image component reference value after filtering.

[0121] Furthermore, the video image component prediction device may first perform filtering on adjacent pixel point positions, and such a process involves filtering the image components awaiting prediction (e.g., chromatic image components), and the embodiments of the present invention are not limited to this. In other words, in the embodiments of the present invention, the video image component prediction device can perform a fourth filtering process on the image component reference values ​​awaiting prediction to obtain the reconstructed values ​​of the image components awaiting prediction.

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

[0123] In some embodiments of the present invention, the process by which a video image component prediction device constructs a component linear model includes constructing a first subcomponent linear model using the largest first image component reference value after filtering, the largest pending image component reference value, and a preset initial linear model; constructing a second subcomponent linear model using the smallest first image component reference value after filtering, the smallest pending image component reference value, and a preset initial linear model; obtaining model parameters based on the first and second subcomponent linear models; and constructing a component linear model using the model parameters and the preset initial linear model.

[0124] In the embodiments of this invention, the preset initial linear model is an initial model in which the model parameters are unknown.

[0125] For example, a preset initial linear model may take the form of equation (1), where α and β are unknown. Using the first subcomponent linear model and the second subcomponent linear model, a second equation with two variables can be constructed to determine the model parameters α and β. Substituting α and β into equation (1), a linear mapping relationship model between the first image component and the image component awaiting prediction can be obtained.

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

Number

[0127] Here, L max and L min represent the maximum value and the minimum value obtained by finding from the first image component reference values corresponding to the un-downsampled left side and / or upper side, and C max and C min represent the predicted image component reference values corresponding to the adjacent reference pixel points at the positions corresponding to L max and L min Figure 7 is a schematic structural diagram showing constructing a prediction model based on the maximum value and the minimum value of the current block. Here, the horizontal coordinate represents the first image component reference value of the current block, the vertical coordinate represents the predicted image component reference value of the current block, and according to L max and L min , and C max and C min , the model parameters α and β can be calculated through formula (2), and the constructed prediction model is C = αY + β. In the actual prediction process, Y represents the first image component reconstruction value corresponding to one pixel point in the current block, and C represents the predicted value of the predicted image component corresponding to the pixel point in the current block.

[0128] Clearly, the video image component prediction device first selects the maximum and minimum first image component reference values ​​based on one or more directly acquired first image component reference values ​​corresponding to the current block, and then constructs a component linear model by performing downsampling (filtering) based on the positions corresponding to the selected maximum and minimum first image component reference values. In this way, the workload of downsampling the pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, which reduces the complexity of constructing the component linear model, further reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

[0129] Based on the above embodiment, the embodiment of the present application provides a video component prediction device, in which each unit of the device and each module contained within each unit can be realized by a processor within the video component prediction device, and of course, can also be realized by specific logic circuits. In the implementation process, the processor may be a central processing unit, a microprocessor, a digital signal processor (DSP), or a field-programmable gate array, etc.

[0130] As shown in Figure 8, the video component prediction device 3 according to the embodiment of the present application is An acquisition unit 30 configured to acquire a set of reference values ​​for the first image component of the current block, wherein the set of reference values ​​for the first image component includes one or more first image component reference values, A determination unit 31 is configured to determine a plurality of first image component reference values ​​from the set of reference values ​​for the first image component, A filtering unit 32 is configured to perform a first filtering process on each of the sample values ​​of pixel points corresponding to the plurality of first image component reference values ​​in order to obtain a plurality of filtered first image reference sample values. The system includes a prediction unit 33 configured to determine the predicted value of the image component awaiting prediction for the current block based on the mapping value, The determination unit 31 further determines a pending image component reference value corresponding to the plurality of filtered first image reference sample values, and the pending image component is a different image component from the first image component. Based on the plurality of filtered first image reference sample values ​​and the pending image component reference value, the parameters of the component linear model are determined, and the component linear model represents a linear mapping relationship that maps the sample value of the first image component to the sample value of the pending image component. The filtering unit 32 is further configured to perform a mapping process on the reconstructed value of the first image component of the current block based on the component linear model, and to obtain a mapping value.

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

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

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

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

[0135] In some embodiments of the present application, the determination unit 31 is further configured to determine one or more reference pixel points located in a position other than the current block. The acquisition unit 30 is further configured to determine the one or more reference pixel points as the one or more first image component reference values.

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

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

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

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

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

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

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

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

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

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

[0146] In some embodiments of the present invention, the determination unit 31 is further configured to construct a first subcomponent linear model using the maximum first image component reference value after filtering, the maximum pending image component reference value, and a preset initial linear model; to construct a second subcomponent linear model using the minimum first image component reference value after filtering, the minimum pending image component reference value, and the preset initial linear model; to obtain model parameters based on the first subcomponent linear model and the second subcomponent linear model; and to construct the component linear model using the model parameters and the preset initial linear model.

[0147] In some embodiments of the present application, the determination unit 31 is further configured such that the parameters of the component linear model include a multiplier factor and an additive offset, calculates a first difference value between the maximum predicted pending image component reference value and the minimum predicted pending image component reference value, calculates a second difference value between the maximum first image component reference value and the minimum first image component reference value, sets the multiplier factor as the ratio of the first difference value and the second difference value, calculates a first product between the maximum first image component reference value and the multiplier factor, and sets the additive offset as the difference value between the maximum predicted pending image component reference value and the first product, or calculates a second product between the minimum first image component reference value and the multiplier factor, and sets the additive offset as the difference value between the minimum predicted pending image component reference value and the second product.

[0148] In some embodiments of the present application, the first image component is a luminance component, and the predicted image component is a first or second chromatic difference component, or The first image component is the first color difference component, and the predicted image component is either the luminance component or the second color difference component, or The first image component is the second color difference component, and the predicted image component is either the luminance component or the first color difference component, or The first image component is the first color component, and the predicted image component is either the second or third color component, or The first image component is the second color component, and the predicted image component is either the first color component or the third color component, or The first image component is the third color component, and the image component awaiting prediction is either the second color component or the first color component.

[0149] In some of the embodiments of this application, the first color component is a red component, the second color component is a green component, and the third color component is a blue component.

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

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

[0152] In actual applications, as shown in Figure 9, the video component prediction device according to the embodiment of the present invention includes a memory 34 configured to store executable video component prediction instructions, The system includes a processor 35 configured to implement the steps of the video component prediction method provided in the above embodiment when executing an executable video component prediction instruction stored in the memory 34.

[0153] In response to this, the embodiment of the present invention provides a computer-readable storage medium in which video component prediction instructions are stored, and when the video component prediction instructions are executed by the processor 35, the steps of the video component prediction method provided in the above embodiment are realized.

[0154] It should be noted that the above description of embodiments of the storage medium and apparatus is the same as the description of embodiments of the method described above, and has the same beneficial effects as the embodiments of the method. Technical details not disclosed in the embodiments of the storage medium and apparatus of this application can be understood by referring to the description of the embodiments of the method of this application.

[0155] The above description is merely a specific embodiment of the present application, and the scope of protection of this application is not limited thereto. Any modification or substitution that a person skilled in the art can easily conceive within the technical scope disclosed herein should be included within the scope of protection of this application. Accordingly, the scope of protection of this application shall be subject to the scope of protection of the claims. [Industrial applicability]

[0156] In the embodiments of the present invention, the video component prediction device first determines a plurality of first image component reference values ​​based on a directly acquired set of reference values ​​for the first image component corresponding to the current block, and then constructs a component linear model by performing a filtering process based on the positions corresponding to the determined plurality of first image component reference values. In this way, the workload of filtering the pixel points corresponding to the current block is reduced, i.e., the filtering operation is reduced, which reduces the complexity of constructing the component linear model, further reduces the complexity of video component prediction, improves prediction efficiency, and improves video encoding and decoding efficiency.

Claims

1. A method for predicting image components, which is applied to a decoder. Determine the reference sample set for the first image component of the current block, The method involves determining a plurality of first image component reference samples from the aforementioned first image component reference sample set, wherein the plurality of first image component reference samples correspond to four preset sample positions. The process involves performing a first filtering process on each of the aforementioned multiple first image component reference samples to obtain multiple filtered first image reference samples. The process involves determining a prediction-awaited image component reference sample corresponding to the multiple filtered first image reference samples, wherein the prediction-awaited image component is a different image component from the first image component. The parameters of the component prediction model are determined based on the plurality of filtered first image reference samples and the image component reference samples awaiting prediction, wherein the component prediction model represents a mapping relationship that maps the first image component samples to the image component samples awaiting prediction, Based on the component prediction model, a mapping process is performed on the reconstructed sample of the first image component of the current block to obtain a mapping sample. This includes determining a predicted sample of the pending image component of the current block based on the mapping sample, Determining multiple first image component reference samples from the aforementioned first image component reference sample set is: This includes determining multiple first image component reference samples in a sample in one or more adjacent rows above the current block, based on two of the four preset sample positions, and determining multiple first image component reference samples in a sample in one or more adjacent columns to the left of the current block, based on two of the four preset sample positions. Image component prediction method.

2. Determining the reference sample set for the first image component of the current block is: This includes determining the adjacent samples of the current block as the reference sample set for the first image component, The image component prediction method according to claim 1.

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

4. Determining the parameters of the component prediction model based on the multiple filtered first image reference samples and the image component reference samples awaiting prediction is: Based on the aforementioned plurality of filtered first image reference samples, the largest filtered first image component reference sample and the smallest filtered first image component reference sample are determined. The process involves determining the largest image component reference sample awaiting prediction, corresponding to the largest first image component reference sample after filtering, and the smallest image component reference sample awaiting prediction, corresponding to the smallest first image component reference sample after filtering. This includes determining the parameters of the component prediction model based on the largest first image component reference sample after filtering, the smallest first image component reference sample after filtering, the largest pending prediction image component reference sample, and the smallest pending prediction image component reference sample. The image component prediction method according to claim 1.

5. Based on the component prediction model, a mapping process is performed on the reconstructed sample of the first image component of the current block to obtain a mapping sample. The second filtering process is performed on the reconstructed sample of the first image component to obtain a second filtered sample of the reconstructed sample of the first image component, This includes performing a mapping process on the second filter sample based on the component prediction model to determine the mapping sample, The image component prediction method according to claim 1.

6. A method for predicting image components, which is applied to an encoder, Determine the reference sample set for the first image component of the current block, The method involves determining a plurality of first image component reference samples from the aforementioned first image component reference sample set, wherein the plurality of first image component reference samples correspond to four preset sample positions. The process involves performing a first filtering process on each of the aforementioned multiple first image component reference samples to obtain multiple filtered first image reference samples. The process involves determining a prediction-awaited image component reference sample corresponding to the multiple filtered first image reference samples, wherein the prediction-awaited image component is a different image component from the first image component. The parameters of the component prediction model are determined based on the plurality of filtered first image reference samples and the image component reference samples awaiting prediction, wherein the component prediction model represents a mapping relationship that maps the first image component samples to the image component samples awaiting prediction, Based on the component prediction model, a mapping process is performed on the reconstructed sample of the first image component of the current block to obtain a mapping sample. This includes determining a predicted sample of the pending image component of the current block based on the mapping sample, Determining multiple first image component reference samples from the aforementioned first image component reference sample set is: This includes determining multiple first image component reference samples in a sample in one or more adjacent rows above the current block, based on two of the four preset sample positions, and determining multiple first image component reference samples in a sample in one or more adjacent columns to the left of the current block, based on two of the four preset sample positions. Image component prediction method.

7. Determining the reference sample set for the first image component of the current block is: This includes determining the adjacent samples of the current block as the reference sample set for the first image component, The image component prediction method according to claim 6.

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

9. Determining the parameters of the component prediction model based on the multiple filtered first image reference samples and the image component reference samples awaiting prediction is: Based on the aforementioned plurality of filtered first image reference samples, the largest filtered first image component reference sample and the smallest filtered first image component reference sample are determined. The process involves determining the largest image component reference sample awaiting prediction, corresponding to the largest first image component reference sample after filtering, and the smallest image component reference sample awaiting prediction, corresponding to the smallest first image component reference sample after filtering. This includes determining the parameters of the component prediction model based on the largest first image component reference sample after filtering, the smallest first image component reference sample after filtering, the largest pending prediction image component reference sample, and the smallest pending prediction image component reference sample. The image component prediction method according to claim 6.

10. Based on the component prediction model, a mapping process is performed on the reconstructed sample of the first image component of the current block to obtain a mapping sample. The second filtering process is performed on the reconstructed sample of the first image component to obtain a second filtered sample of the reconstructed sample of the first image component, This includes performing a mapping process on the second filter sample based on the component prediction model to determine the mapping sample, The image component prediction method according to claim 6.

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

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

13. A non-volatile computer-readable medium that stores a program and a bitstream, wherein the program causes a processor to execute the image component prediction method described in any one of claims 6 to 10 to generate the bitstream.

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