Cross-Component Sample Adaptive Offset for Video Coding
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Solution Overview
Problem
Current video coding standards face challenges in efficiently encoding and decoding video data, particularly in maintaining image quality while reducing bit-rate, especially with the increasing resolution of digital video from HD to 4K and beyond.
Innovation Solution
The implementation of Cross-Component Sample Adaptive Offset (CCSAO) method, which improves chroma coding efficiency by exploring the cross-component relationship between luma and chroma components. This involves receiving a video signal with luma and chroma components, utilizing sample values of the luma component to classify the chroma component, and applying offsets based on the classification to modify the chroma component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If conventional video coding standards are used to encode high-resolution video, then video resolution is improved, but bit-rate consumption increases significantly
Solution Approach 1:
The patent introduces luma component sample values as an intermediary to classify chroma component samples. By using the luma component (which has better prediction accuracy) to guide the classification of chroma samples, the system indirectly improves chroma prediction without directly transmitting more chroma data, thus reducing bit-rate while maintaining resolution
Solution Approach 2:
The patent changes the parameter used for chroma prediction by utilizing luma sample values to determine classification categories. Instead of relying solely on chroma sample values, the system transforms the classification parameter from chroma-based to luma-based, achieving better compression efficiency at high resolutions
2Quantity of substance
If chroma coding efficiency is improved by exploring cross-component relationships, then bit-rate savings are achieved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using luma sample values from specific positions (collocated and neighboring blocks) to classify chroma samples. This localized approach uses only necessary reference samples rather than processing entire blocks, reducing computational complexity while achieving bit-rate savings through targeted cross-component utilization
Data Source
AI summary
An electronic apparatus performs a method of video coding. The method comprises: obtaining a video picture that includes a first component and a second component; determining a plurality of offsets associated with the second component; utilizing a sample value of the first component to obtain a class index associated with the second component; selecting an offset from the plurality of offsets for the second component according to the class index; and obtaining a sample value of the second component based on the selected offset, wherein the sample value of the first component is derived from one or more of collocated or neighboring samples of the first component relative to a sample of the second component, and wherein the sample value of the first component is derived differently for different chroma formats.