Luma-Guided Chroma Loop Filtering for Lower-Bitrate Image Coding
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality image/video data, particularly in immersive media formats like VR and AR, poses challenges in efficient compression, transmission, and storage due to the higher bit rates, leading to increased costs and inefficiencies in existing wired/wireless broadband and storage solutions.
Innovation Solution
An adaptive loop filtering-based method and apparatus that modifies reconstructed chroma samples based on luma samples, enabling cross-component adaptive loop filtering (CCALF) with signaled information on filter coefficients and filter set indices, allowing for efficient encoding and decoding of images/videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image/video data is transmitted using existing wired/wireless broadband lines or storage mediums, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies adaptive loop filtering with cross-component adaptive loop filtering (CCALF) to modify filtering parameters dynamically. The filter coefficients and filter set indices are adapted based on local image characteristics, allowing the system to maintain high image quality while optimizing compression efficiency. This reduces the bit rate required for transmission and storage, thereby lowering transmission and storage costs without sacrificing image quality.
2Productivity
If existing compression technology is used for high-resolution image/video, then transmission is possible, but compression efficiency is insufficient
Solution Approach 1:
The patent implements adaptive loop filtering that operates on local regions of the image/video data. The filter coefficients are determined based on local statistical properties of the reconstructed samples, allowing different filtering strengths to be applied to different regions. This local adaptation improves compression efficiency by preserving important local details while compressing less critical areas more aggressively, thereby reducing overall bit rate requirements.
Solution Approach 2:
The patent employs dynamic filtering where the filter coefficients and filter set indices are not fixed but are adapted based on the local characteristics of the reconstructed samples. The adaptive loop filter dynamically adjusts its parameters according to the statistical properties of the image data in each region, improving compression efficiency by optimizing the trade-off between quality and bit rate for each local area rather than applying a uniform filtering approach.
3Manufacturing precision
If conventional filtering is applied to reconstructed chroma samples, then chroma quality is improved, but filtering cannot leverage luma sample information
Solution Approach 1:
The patent merges the filtering processes for luma and chroma components through cross-component adaptive loop filtering (CCALF). The filter coefficients for chroma samples are derived by combining information from both luma and chroma reconstructed samples. This merging allows the chroma filtering to leverage the statistical information from luma samples, improving chroma quality while maintaining filtering adaptability through the combined use of both component data.
Data Source
AI summary
According to one embodiment of the present document, filtering for chroma blocks can be performed on the basis of luma blocks. For example, a cross component adaptive loop filtering process can be performed. Therefore, the accuracy of in-loop filtering can be improved.