Video Coding With Cross-Component Residual Chroma Estimation
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Solution Overview
Problem
Conventional video coding techniques suffer from suboptimal coding gain and efficiency, particularly in handling chroma residual blocks, which affects the overall performance of video encoding and decoding processes.
Innovation Solution
Implementing a cross-component residual model (CCRM) to estimate chroma residual blocks based on luma residual blocks, enhancing the conversion process to improve coding efficiency and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding techniques are used for chroma residual block handling, then the coding process is simple, but the coding gain and efficiency are suboptimal
Solution Approach 1:
The patent introduces a cross-component residual model (CCRM) as an intermediary mechanism that estimates chroma residual blocks based on luma residual blocks. This mediator leverages the correlation between luma and chroma components to improve coding efficiency without requiring completely separate chroma residual processing, thus balancing performance improvement with manageable complexity
Solution Approach 2:
The patent applies parameter changes by using luma residual block values to estimate chroma residual block values through the CCRM. By changing the approach from independent chroma residual coding to correlated estimation based on luma parameters, the coding efficiency is improved while maintaining reasonable computational complexity
2Measurement precision
If conventional chroma prediction methods are used, then the processing is computationally simple, but the prediction accuracy and coding performance are insufficient
Solution Approach 1:
The CCRM acts as an intermediary that improves chroma prediction accuracy by utilizing luma residual information. Instead of directly computing complex chroma predictions from scratch, the model uses luma residuals as an intermediate step to derive more accurate chroma predictions, thereby improving precision without proportionally increasing computational complexity
Solution Approach 2:
The patent applies universality by using the same CCRM framework for both luma and chroma components. The model serves multiple functions: it processes luma residuals and generates improved chroma predictions, making the system more efficient and accurate without requiring entirely separate processing paths
Data Source
AI summary
Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: obtaining, for a conversion between a video unit of a video and a bitstream of the video, a cross-component residual model (CCRM) estimated chroma residual block related to the video unit by applying a CCRM model to a luma residual block related to the video unit; and performing the conversion based on the CCRM estimated chroma residual block.