LMCS Luma Mapping and Chroma Scaling for Lower Coding Complexity
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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, has led to higher transmission and storage costs due to the increased amount of information, necessitating a more efficient compression technology.
Innovation Solution
Implementing luma mapping with chroma scaling (LMCS) processes that include efficient filtering, constrained LMCS codewords, linear mapping, and simplified index derivation for chroma residual scaling, reducing resource requirements and latency in the encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution, high-quality image/video data is transmitted or stored using existing media and storage systems, then image quality is maintained, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies luma mapping with chroma scaling (LMCS) to transform the parameter distribution of image data. By remapping luma values and scaling chroma residuals, the technique changes the statistical parameters of the data to achieve better compression characteristics, reducing the bits required to represent the same visual quality
2Productivity
If luma mapping with chroma scaling (LMCS) process is performed to improve compression efficiency, then compression ratio increases, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the LMCS process into distinct stages: luma mapping, chroma residual scaling, and selective filtering. By dividing the complex transformation into manageable segments, each can be optimized independently, reducing overall computational complexity while maintaining compression efficiency
Solution Approach 2:
The patent implements conditional filtering where filters are applied selectively based on content characteristics. The filtering process serves itself by automatically determining when and where to apply compression operations, avoiding unnecessary computations in regions where filtering would not improve quality
3Manufacturing precision
If efficient filtering is applied in LMCS process, then visual quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies filtering operations locally and selectively rather than uniformly across all image data. By identifying specific regions that benefit from filtering and applying operations only there, the technique improves visual quality in critical areas while minimizing overall processing time
Data Source
AI summary
According to the disclosure of the present document, the index derivation process for the inverse luma mapping and/or the chroma residual scaling may be simplified, and the linear LMCS may be applied. Accordingly, the complexity of the LMCS may be reduced and video/image coding efficiency may be increased.