Cross-Component Parameter Updates for Chroma Prediction Accuracy
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Solution Overview
Problem
Existing video coding methods using Cross-Component Linear Model (CCLM) for predicting chroma samples face inefficiencies due to noisy or misrepresentative reconstructed reference samples, leading to suboptimal coding efficiency when there is a weak correlation between luma and chroma channels.
Innovation Solution
An apparatus and method for updating linear model parameters, including a slope and offset parameter, with adaptive granularity based on prediction block characteristics, and signaling these updates in a bitstream to improve correlation between luma and chroma channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CCLM is used to predict chroma samples using reconstructed reference samples, then coding efficiency is improved when strong linear correlation exists between luma and chroma channels, but prediction accuracy deteriorates when reference samples are noisy or misrepresentative
Solution Approach 1:
The patent applies parameter changes by introducing a correction term that modifies the slope parameter of the linear model based on the correlation strength between luma and chroma channels. When correlation is weak, the correction term adjusts the slope to reduce over-reliance on potentially noisy reference samples, thereby maintaining prediction accuracy while preserving coding efficiency benefits.
Solution Approach 2:
The patent implements feedback by calculating the correlation strength between luma and chroma channels and using this information to dynamically adjust the linear model parameters. The correction term is derived from the actual correlation measurement, creating a closed-loop system that adapts the prediction model based on observed data characteristics.
2Reliability
If linear model parameters are updated with fine granularity, then prediction accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
The patent applies local quality by differentiating the update mechanism based on the specific characteristics of each prediction block. The correction term is selectively applied only when the correlation strength indicates potential issues with reference sample representativeness, rather than uniformly applying updates to all blocks. This conditional approach maintains accuracy where needed while reducing unnecessary processing complexity.
Solution Approach 2:
The patent implements dynamics by making the linear model parameters adaptive rather than fixed. The slope parameter is dynamically adjusted based on the measured correlation strength between luma and chroma channels in each block, allowing the system to automatically adapt to varying content characteristics without requiring manual configuration or complex processing.
Data Source
AI summary
A method comprising: calculating at least two parameters (a, b) that define a mapping from a first color component (Y) to a second color component (Cb/Cr), the at least two parameters (a, b) including at least a slope parameter (a) and an offset parameter (b); determining an update term (u) to the slope parameter (a); applying the update term (u) to the slope parameter (a) by adding the update term (u) to the value of the slope parameter (a) to generate an updated slope parameter (a); determining a reference value (yr) of first color component (Y); and calculating an updated offset parameter (b) based on the reference value (yr) of the first color component (Y) and the updated slope parameter (a′)