Cross-Component Sample Adaptive Offset Memory Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video coding standards face challenges in efficiently processing chroma samples due to the requirement for cross-component sample adaptive offset, which necessitates additional luma samples not stored in the line buffer, leading to increased memory requirements.
Innovation Solution
The proposed method determines an index based on the vertical coordinate of a chroma sample, selects a luma sample based on this index, classifies the chroma sample using a reconstructed luma sample value, calculates an offset based on the classification, and adds this offset to the reconstructed chroma sample value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cross-component sample adaptive offset is applied to process chroma samples, then video coding efficiency is improved, but memory requirements increase due to the need for additional luma samples not stored in the line buffer
Solution Approach 1:
The patent uses co-located luma samples from the line buffer as substitutes for the required luma samples that would otherwise need to be stored separately. By copying/reusing existing luma samples that are already available in the line buffer, the system avoids the need for additional memory storage while still enabling the cross-component sample adaptive offset process to proceed with the necessary sample data.
2Quantity of substance
If co-located luma samples not stored in the line buffer are replaced with nearest available luma samples, then memory usage is optimized, but processing complexity increases
Solution Approach 1:
The patent applies different handling strategies for different spatial locations of chroma samples. Specifically, it determines whether to use co-located luma samples or nearest available luma samples based on the vertical coordinate of the chroma sample within the picture. This localized approach optimizes memory usage for certain regions while maintaining processing simplicity in other regions, balancing the trade-off between memory optimization and processing complexity.
Data Source
AI summary
The present disclosure provides a video data processing method for cross-component sample adaptive offset (CCSAO). The method includes determining an index based on a vertical coordinate of a chroma sample within a picture; determining a luma sample based on the index; classifying the chroma sample based on a reconstructed value associated with the luma sample; determining an offset based on the classification; and adding the offset to a reconstructed value associated with the chroma sample.