Cross-Component Chroma Prediction With Five-Tap MH-CCP
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Solution Overview
Problem
Existing video coding methods struggle to effectively utilize redundancy in video data for efficient compression, particularly in lossy compression scenarios where distortion tolerance varies by application, leading to suboptimal compression ratios and quality trade-offs.
Innovation Solution
Implementing multi-hypothesis cross-component prediction (MH-CCP) that uses a weighted sum of multiple luma samples, including a collocated luma sample and filtered neighboring samples, to predict chroma samples, with a five-tap model determining weighing factors based on a reference area for each coding block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is used to reduce data volume, then bandwidth and storage requirements are reduced, but video quality deteriorates
Solution Approach 1:
The patent applies parameter changes by using a five-tap model instead of traditional fewer-tap models, and by dynamically adjusting weighting factors based on reference area characteristics. This allows the compression algorithm to adapt parameters (weighting factors, tap selections) to maintain chroma prediction accuracy while achieving higher compression ratios through more efficient redundancy exploitation.
Solution Approach 2:
The patent introduces dynamics by making the prediction model adaptive - selecting different tap combinations and weighting factors based on local image characteristics in the reference area. This dynamic adaptation allows the system to optimize compression efficiency for different content types while maintaining quality, resolving the contradiction between compression ratio and video quality.
2Productivity
If traditional prediction methods are used, then device complexity is low, but compression efficiency is insufficient
Solution Approach 1:
The patent segments the prediction process into multiple hypotheses, each using different tap combinations (e.g., horizontal, vertical, diagonal patterns). By dividing the complex prediction task into multiple simpler hypothesis models and selecting the best one, the system achieves higher compression efficiency without excessively increasing overall device complexity, as each individual hypothesis remains computationally manageable.
Solution Approach 2:
The patent uses a five-tap model which provides more prediction points than traditional models. This excessive action (using more taps than strictly necessary) allows for better exploitation of local redundancy and improved prediction accuracy, thereby enhancing compression efficiency while the added complexity is managed through efficient implementation of the extended model.
3Productivity
If higher compression ratios are achieved, then bandwidth requirements are reduced, but distortion increases
Solution Approach 1:
The patent employs feedback mechanisms by using reference areas from previously decoded blocks to dynamically determine weighting factors for current block prediction. This feedback loop allows the system to adapt to local image characteristics and maintain prediction accuracy even at higher compression ratios, thereby reducing distortion while achieving better compression through intelligent parameter adjustment based on historical data.
Data Source
AI summary
The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current coding block of an image frame and signals a first syntax element for a multi-hypothesis cross-component prediction (MH-CCP) mode. A computing system determines that the MH-CCP mode is enabled to reconstruct each of a plurality of chroma samples of the current coding block based on a respective luma sample collocated with the respective chroma sample and one or more neighboring luma samples. The computing device identifies a five tap model configured to determine chroma samples of the current coding block in the MH-CCP mode. Based on the five tap model, a pair of neighboring luma samples of a first luma sample is identified to generate a first chroma sample collocated with the first luma sample. The computing system reconstructs the current coding block including the first chroma sample.