Simplified Cross-Component Linear Models for Chroma Reconstruction
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Solution Overview
Problem
The challenge of efficiently encoding and decoding high-definition video data while maintaining image quality is exacerbated by the exponential growth in data volume, particularly in reconstructing chroma blocks during video data processing.
Innovation Solution
A cross-component linear model is employed to reconstruct chroma blocks by identifying maximum and minimum luma samples, computing down-sampled luma samples, and generating a linear model to predict chroma samples based on these luma samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional chroma block reconstruction methods are used, then encoding and decoding can be performed, but image quality deteriorates and processing efficiency is low
Solution Approach 1:
The patent introduces a cross-component linear model as an intermediary mechanism that establishes a mathematical relationship between luma and chroma components. This model uses luma sample information (which is already processed) to predict and reconstruct chroma samples, serving as a mediator that improves chroma reconstruction quality without requiring separate complex chroma processing
Solution Approach 2:
The patent changes the approach from traditional chroma processing by transforming the problem into the luma domain. Instead of processing chroma samples directly, the method uses luma sample parameters (maximum, minimum, and their positions) to derive chroma information through linear models, effectively changing the processing parameter space to achieve better results
2Manufacturing precision
If chroma blocks are reconstructed using conventional methods, then processing can be completed, but image quality is not maintained in high-definition video
Solution Approach 1:
The patent extracts only the essential information needed for chroma reconstruction from the luma component. Instead of processing all chroma data separately, the method extracts maximum and minimum luma sample values and their positions, then uses these extracted parameters to reconstruct the entire chroma block, significantly reducing the effective data volume that needs direct processing
3Manufacturing precision
If high-definition video data is processed, then image quality is maintained, but data volume grows exponentially
Solution Approach 1:
The patent merges the chroma reconstruction process with the existing luma processing pipeline. By using the luma component's maximum and minimum sample information to predict chroma values, the method combines what would traditionally be separate processing steps into a unified approach, effectively reducing the independent data volume that needs processing while maintaining HD image quality
Data Source
AI summary
A computing device performs a method of decoding video data by reconstructing a luma block corresponding to a chroma block; searching a sub-group of a plurality of reconstructed neighboring luma samples in a predefined order to identify a maximum luma sample and a minimum luma sample; computing a down-sampled maximum luma sample corresponding to the maximum luma sample; computing a down-sampled minimum luma sample corresponding to the minimum luma sample; generating a linear model using the down-sampled maximum luma sample, the down-sampled minimum luma sample, the first reconstructed chroma sample, and the second reconstructed chroma sample; computing down-sampled luma samples from luma samples of the reconstructed luma block, wherein each down-sampled luma sample corresponds to a chroma sample of the chroma block; and predicting chroma samples of the chroma block by applying the liner model to the corresponding down-sampled luma samples.