Cross-Component Sample Offset for Luma-Chroma Video Coding
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing luma and chroma components due to the lack of effective methods that exploit the cross-component relationships between them, leading to suboptimal coding efficiency.
Innovation Solution
Implementing a method and apparatus that utilize cross-component sample adaptive offset (CCSAO) techniques to classify and modify samples based on edge components, edge directions, and strengths, enhancing the coding efficiency of both luma and chroma components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding methods are used to compress luma and chroma components independently, then the coding process is simple, but the coding efficiency is suboptimal due to lack of cross-component relationship exploitation
Solution Approach 1:
The patent merges the coding processes of luma and chroma components by applying cross-component sample adaptive offset (CCSAO) where chroma samples are classified and offset-adjusted based on luma component characteristics. This integration exploits cross-component relationships to improve coding efficiency while maintaining a unified coding framework.
Solution Approach 2:
The patent applies local quality by performing sample-wise classification of chroma samples based on their corresponding luma sample characteristics. Different chroma samples receive different offset adjustments according to their local luma edge properties, allowing adaptive optimization at the sample level rather than uniform processing.
2Productivity
If cross-component sample adaptive offset techniques are implemented to exploit relationships between luma and chroma components, then coding efficiency is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of chroma samples based on luma edge detection before applying offset adjustments. By pre-classifying samples into different groups based on their luma component characteristics, the method prepares the data structure in advance to enable efficient offset application without requiring complex real-time computations during the main coding process.
Solution Approach 2:
The patent segments chroma samples into different classes based on their corresponding luma sample properties (e.g., edge vs. non-edge regions). This segmentation allows the application of different offset strategies to different sample groups, simplifying the computational approach by breaking down the complex cross-component relationship into manageable categories.
3Measurement precision
If sample classification based on edge components and edge directions is performed, then the precision of sample offset adjustment is improved, but the processing time increases
Solution Approach 1:
The patent applies partial action by performing edge detection and classification only on luma samples that are relevant to chroma prediction (i.e., collocated luma samples). Rather than processing all luma samples in the frame, the method focuses computational effort only on the subset of samples that directly influence chroma offset decisions, reducing overall processing time while maintaining classification precision where needed.
Data Source
AI summary
Methods and devices are provided for video coding. In one method, a decoder may obtain an edge component syntax element that indicates a first component for classifying a second component, where the first component and the second component are different components of a plurality of components in a picture frame. Furthermore, the decoder may obtain a classifier for a current sample of the second component according to the edge component syntax element, an edge direction, and one or more edge strengths of a collocated sample of the first component, obtain a class corresponding to the current sample of the second component by classifying the current sample of the second component according to the classifier, and obtain a sample offset for the current sample of the second component according to the class and modify the current sample of the second component based on the sample offset.