Prediction Dependent Residual Scaling for Video Coding
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Solution Overview
Problem
Current video coding standards, such as VVC, face challenges with increased computational complexity and on-chip memory requirements due to domain mappings in Luma Mapping with Chroma Scaling (LMCS), as well as latency issues from sequential applications of complex inter mode coding tools, which complicate the decoding process and reduce efficiency.
Innovation Solution
The proposed Prediction Dependent Residual Scaling (PDRS) method scales prediction residuals directly without sample mapping, using luma prediction samples to derive scaling factors and maintain all decoding operations in the original domain, eliminating the need for forward and inverse mapping operations and reducing complexity and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Luma Mapping with Chroma Scaling (LMCS) with domain mappings is used, then coding efficiency is improved, but computational complexity and on-chip memory requirements increase
Solution Approach 1:
The patent extracts and removes the forward mapping and inverse mapping operations from the decoding process. By using prediction-dependent residual scaling instead of domain mappings, the complex mapping functions are taken out of the system, reducing computational complexity while maintaining chroma scaling functionality.
Solution Approach 2:
The patent introduces prediction samples as an intermediary mechanism. Instead of using complex forward/inverse mapping functions to transform domains, the prediction samples serve as a mediator to derive scaling factors for chroma residuals, achieving similar coding efficiency with lower computational complexity.
2Productivity
If Luma Mapping with Chroma Scaling (LMCS) with domain mappings is used, then coding efficiency is improved, but on-chip memory requirements increase
Solution Approach 1:
The patent removes the need for storing forward mapping and inverse mapping tables in on-chip memory. By replacing domain mappings with prediction-dependent residual scaling, the memory-intensive mapping tables are extracted from the system, reducing on-chip memory requirements while maintaining coding efficiency.
3Manufacturing precision
If complex inter mode coding tools are applied sequentially, then coding accuracy is improved, but decoding latency increases
Solution Approach 1:
The patent performs prediction sample generation as a preliminary action before the complex inter mode coding tools are applied. By deriving scaling factors from prediction samples obtained at an earlier stage, the patent enables parallel processing of chroma residual scaling, reducing decoding latency while maintaining coding accuracy.
Data Source
AI summary
Methods are provided for reducing the computation complexity and on-chip memory requirements as well as the decoding latency introduced by LMCS. In one method, a luma prediction sample is obtained for decoding a luma residual sample, a scaling factor is derived using the luma prediction sample, the scaling factor is used to scale the luma residual sample, and the reconstructed luma sample is calculated by adding the luma prediction sample and the scaled luma residual sample.