Cross-Component Prediction Models for Efficient Chroma Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency and effectiveness, particularly in handling cross-component prediction for chroma components in video encoding and decoding processes.
Innovation Solution
The proposed solution involves determining a cross-component prediction (CCP) model for video blocks based on a history table or a list of CCP candidates, using techniques such as cross-component linear model prediction and intra block copy, to enhance prediction accuracy and reduce redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video coding technologies are used for cross-component prediction, then the coding process is simpler, but coding efficiency and effectiveness are insufficient
Solution Approach 1:
The patent segments the cross-component prediction process by maintaining a history table that stores multiple CCP models and a candidate list that stores multiple CCP candidates. This segmentation allows the system to select appropriate models and candidates for different video blocks, improving coding efficiency through targeted prediction while managing complexity through structured organization of prediction elements.
Solution Approach 2:
The patent implements dynamics by adaptively selecting CCP models from the history table and CCP candidates from the candidate list based on the characteristics of each current video block. This dynamic selection process allows the system to optimize prediction accuracy for different block types and content characteristics, thereby improving coding efficiency without requiring a fixed complex structure for all cases.
2Measurement precision
If a history table of CCP models and list of CCP candidates are maintained, then prediction accuracy is improved, but memory usage and processing complexity increase
Solution Approach 1:
The patent extracts only the essential and most effective CCP models and candidates into the history table and candidate list, rather than maintaining all possible prediction models. This extraction process improves prediction accuracy by focusing on the most relevant models while limiting the increase in memory usage and processing complexity to only the necessary elements.
Solution Approach 2:
The patent applies local quality by maintaining different CCP models and candidates for different types of video blocks based on their specific characteristics. Rather than using a uniform set of models for all blocks, the system tailors the selection of CCP models and candidates to local block properties, improving prediction accuracy while managing overall system complexity through localized optimization.
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, at least one target cross component prediction (CCP) model for the current video block is determined based on a history table of CCP models or a list of CCP candidates. A prediction of the current video block is determined based on CCP information of the at least one target CCP model. The conversion is performed based on the prediction.