CCSO Filter Shapes for Chroma Reconstruction Error Reduction
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently reducing reconstruction errors in video data compression, particularly in handling cross-component offset filtering for color components, which affects video quality and compression efficiency.
Innovation Solution
Implementing a cross-component offset filtering method that uses reconstructed color samples and neighboring samples to derive offset values for adjusting reconstruction values, with syntax elements defining filter shapes and modes for luma and chroma components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-component offset filtering is applied to reduce reconstruction errors, then video quality improves, but computational complexity and processing time increase
Solution Approach 1:
The filtering process is divided into distinct stages: offset calculation from luma samples, separate filtering application to chroma components, and selective application based on syntax elements. This segmentation allows the complex filtering operation to be broken down into manageable steps that can be optimized independently.
Solution Approach 2:
The patent applies filtering selectively rather than universally - using syntax elements to determine when CCSO mode is enabled, and applying filters only to specific chroma components where reconstruction errors are most problematic. This partial action approach reduces overall computational complexity while maintaining video quality in critical areas.
2Measurement precision
If multiple filter shapes are supported for different color components, then filtering precision improves, but syntax element complexity and processing overhead increase
Solution Approach 1:
Different filter shapes (vertical, horizontal, diagonal) are applied selectively to different chroma components (Cb, Cr) based on the local characteristics of the video content and the specific component being processed. This allows the system to use the most appropriate filter for each local situation rather than applying a single filter universally.
Solution Approach 2:
The filter shape and application are dynamically selected based on syntax elements that indicate the current processing context, color component type, and filtering conditions. This dynamic adaptation allows the system to optimize filtering precision for each specific case while managing complexity through standardized selection criteria.
3Measurement precision
If cross-component offset filtering is applied to all chroma components, then color accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system uses syntax elements embedded in the bitstream to self-determine when and where filtering should be applied. The decoder reads these syntax elements and automatically activates or deactivates CCSO mode for specific components, eliminating the need for complex external control logic and enabling efficient processing decisions to be made locally during decoding.
Solution Approach 2:
The patent changes the parameter of filter application from a fixed universal approach to a variable selective approach, where the filtering parameter (enabled/disabled) changes based on syntax element values, color component type, and local video characteristics. This allows processing speed to be optimized by skipping filtering in areas where it provides minimal benefit.
Data Source
AI summary
Various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current image frame, a first syntax element for a cross-component sample offset (CCSO) mode, and a second syntax element for defining a filter shape of a loop filter applied in the CCSO mode. When the first syntax element indicates that CCSO mode is enabled, a set of luma samples are identified based on the second syntax element and include a first luma sample collocated with a first color sample and a plurality of neighboring luma samples of the first luma sample. The loop filter is applied to combine the set of luma samples to generate a first sample offset of a first color sample of a first color component. The current image frame is reconstructed at least by adjusting the first color sample based on the first sample offset.