Cross Component Adaptive Loop Filtering for Image Coding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and videos, such as 4K or 8K, poses challenges in efficient compression, transmission, storage, and reproduction, leading to higher costs and reduced visual quality.
Innovation Solution
The implementation of a cross-component adaptive loop filtering (CCALF) process and efficient filtering methods in image/video coding systems, which modify reconstructed chroma samples based on luma samples, enhance compression efficiency and visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image/video data is transmitted or stored, then visual quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies adaptive loop filtering with dynamically adjusted filter coefficients based on local image characteristics and block types. By changing filtering parameters adaptively rather than using fixed strong filtering, the system maintains high visual quality while reducing the bit depth required for chroma components, thus lowering transmission and storage costs.
Solution Approach 2:
The patent implements different filtering strategies for different chroma blocks based on their characteristics (e.g., luma block type, presence of transform coefficients). This local adaptive approach allows optimal filtering strength for each region, preserving quality where needed while compressing data where possible, resolving the contradiction between quality and data量.
2Productivity
If cross-component adaptive loop filtering (CCALF) is applied, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent divides the filtering process into discrete stages: determining chroma block characteristics, selecting appropriate filter coefficients based on luma block type, and applying the filter. This segmentation of the CCALF process into manageable steps reduces implementation complexity while maintaining compression efficiency.
Solution Approach 2:
The patent uses luma block characteristics as an intermediary to guide chroma filtering decisions. Instead of directly analyzing complex chroma data, the system uses the simpler luma block type information as a mediator to select filter coefficients, simplifying the overall processing complexity while achieving efficient compression.
Data Source
AI summary
According to one embodiment of the present document, a cross component adaptive loop filtering (CCALF) process may be performed. The CCALF process can enhance the filtering performance for chroma components and improve the subjective/objective image quality of a picture.