Cross-Component Linear Model Prediction Filter Adaptation
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Solution Overview
Problem
Existing video coding technologies face challenges in achieving high compression ratios with minimal sacrifice in picture quality, particularly in streaming and storage applications where bandwidth and memory resources are limited.
Innovation Solution
The method involves determining a filter for luma samples based on the chroma format of a picture, applying this filter to reconstructed luma samples to obtain filtered reconstructed luma samples, and using these samples as input for cross-component linear model derivation to perform chroma prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed to reduce data size for transmission and storage, then bandwidth and memory resources are saved, but picture quality deteriorates
Solution Approach 1:
The patent applies different filtering operations based on the chroma format parameter (4:4:4, 4:2:2, or 4:2:0). By changing the filtering parameter according to the chroma format, the system achieves better compression efficiency while maintaining picture quality for each specific format, thus resolving the contradiction between data size reduction and quality preservation.
2Productivity
If compression techniques are improved to increase compression ratio, then bandwidth consumption is reduced, but picture quality sacrifice increases
Solution Approach 1:
The patent applies different filtering strategies to different luma sample locations based on their chroma format. Specifically, boundary luma samples use one filtering approach while internal luma samples use another. This local differentiation allows the system to achieve high compression ratios while preserving picture quality in critical areas, thus resolving the contradiction between compression ratio and quality preservation.
3Measurement precision
If adaptive filtering is applied to different luma samples based on position and chroma format, then chroma prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the luma samples into two categories: boundary luma samples and internal luma samples. Different filtering operations are applied to each segment based on the chroma format. This segmentation approach improves chroma prediction accuracy by treating different regions differently, while keeping the complexity manageable through clear categorization and systematic processing of each segment.
Data Source
AI summary
The disclosure relates to methods and a coding apparatus for intra prediction using a linear model. The method includes: determining a filter for a luma component of a current block based on a chroma format of a picture that the current block belongs to, and applying the determined filter to an area of reconstructed luma samples of the luma component of the current block and/or luma samples in selected position neighboring to the current block, to obtain filtered reconstructed luma samples. The method further includes: obtaining, based on the filtered reconstructed luma samples, linear model coefficients. Cross-component prediction is performed based on linear model coefficients of the linear model derivation and the filtered reconstructed luma sample.