Chroma Prediction Weighting for Cross-Component Linear Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In video coding standards like H.266/VVC, the prediction accuracy of chroma values is reduced due to deviations in neighboring reference luma and chroma values, leading to inaccuracies in the linear model used for cross-component linear model prediction.
Innovation Solution
A method for predicting video color components that considers correlations and similarities between neighboring reference and reconstructed values to determine weight coefficients, constructing a more accurate linear model for chroma prediction by weighting neighboring samples based on their deviation from the current coding block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a linear model is constructed using neighboring reference luma values and chroma values, then chroma prediction can be performed, but prediction accuracy deteriorates when reference values deviate greatly from current coding block parameters
Solution Approach 1:
The patent introduces a weight coefficient that dynamically adjusts the influence of neighboring reference samples based on their correlation with the current coding block. By changing the parameter (weight coefficient) according to the deviation degree, the linear model adapts to different scenarios, maintaining accuracy even when reference values deviate significantly from current block parameters
Solution Approach 2:
The patent employs a feedback mechanism by calculating the correlation between neighboring reference samples and current coding block parameters, then using this correlation information to adjust the weight coefficients. This feedback loop ensures that the linear model continuously adapts to the actual data characteristics, improving prediction accuracy while accounting for deviations in reference values
2Measurement precision
If weight coefficients are introduced to account for deviation degrees, then prediction accuracy improves, but calculation complexity increases
Solution Approach 1:
The patent applies local quality by introducing weight coefficients that are specific to each neighboring reference sample based on its individual correlation with the current coding block. Instead of using a uniform approach, each reference sample is treated differently according to its local characteristics, improving accuracy without requiring a complete redesign of the prediction framework
Solution Approach 2:
The weight coefficients are dynamically determined based on the correlation between neighboring reference samples and current coding block parameters. This dynamic adjustment allows the model to adapt to varying conditions without requiring complex manual configuration or multiple static models, balancing accuracy improvement with computational feasibility
Data Source
AI summary
A method for reading a bitstream, a method for storing a bitstream and a method for transmitting a bitstream are provided. The method includes: a bitstream is read, and the following operations are executed to decode the bitstream: acquiring a luma component neighboring reference value, a chroma component neighboring reference value and a luma component reconstructed value corresponding to a current block; generating a chroma component predicted value corresponding to the current block at least based on a linear model corresponding to a factor; and decoding a video based on the chroma component predicted value.