Cross-Component Prediction Candidate Reordering in Video Coding
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Solution Overview
Problem
Existing video coding technologies, such as HEVC and VVC, face challenges in improving coding efficiency and effectiveness, particularly in handling cross-component prediction (CCP) processes.
Innovation Solution
A method is proposed that involves determining a cross-component prediction (CCP) candidate list for a video block, reordering the candidates, and performing conversions based on the reordered list to enhance coding efficiency and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding techniques (HEVC, VVC) are used for cross-component prediction, then the coding process can be completed, but coding efficiency and effectiveness are insufficient
Solution Approach 1:
The patent applies preliminary action by constructing a candidate list of cross-component prediction modes before the actual conversion process. The method pre-defines multiple CCP modes (first CCP mode, second CCP mode, third CCP mode) with different characteristics, allowing the encoder to select the most appropriate mode in advance based on the specific video block characteristics, thereby improving both coding efficiency and effectiveness without requiring complex real-time computations during conversion.
Solution Approach 2:
The patent implements parameter changes by varying the cross-component prediction parameters (such as prediction angles, component selections, and transformation coefficients) across different CCP modes. By changing these parameters adaptively for different video blocks and components, the method optimizes the conversion process to achieve better coding performance while maintaining flexibility for diverse video content types.
2Reliability
If cross-component prediction is performed without optimization, then the conversion can be completed, but coding effectiveness and efficiency are not improved
Solution Approach 1:
The patent applies segmentation by dividing the cross-component prediction process into distinct modes (first CCP mode, second CCP mode, third CCP mode), each handling specific scenarios or video block characteristics. This segmentation allows the complex CCP process to be broken down into manageable, specialized sub-processes, improving coding effectiveness for different video content types while keeping each individual mode relatively simple and computationally efficient.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, a cross component prediction (CCP) candidate list of the current video block is determined, the CCP candidate list comprising at least one CCP candidate. The at least one CCP candidate in the CCP candidate list is reordered. The conversion is performed based on the reordered CCP candidate list.