Cross-Component Linear Chroma Prediction With Down-Sampled Luma
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Solution Overview
Problem
The challenge of efficiently encoding and decoding digital video data, particularly for high-definition and 4K×2K or 8K×4K resolutions, lies in maintaining image quality while optimizing data efficiency.
Innovation Solution
A method and system for reconstructing chroma blocks using a cross-component linear model during video data encoding and decoding, involving the computation of down-sampled luma samples and applying a linear model to predict chroma samples based on reconstructed luma samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If chroma blocks are encoded and decoded using conventional methods, then the encoding and decoding process can be completed, but image quality deteriorates and data redundancy remains high
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 values as predictors to reconstruct chroma sample values, thereby reducing chroma data redundancy while maintaining image quality. The linear model acts as a mediator that enables efficient chroma block reconstruction without requiring full chroma data to be encoded and decoded.
2Productivity
If chroma blocks are reconstructed using cross-component linear model, then data efficiency improves, but computational complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming the chroma reconstruction process into a linear mathematical model with specific parameters (slope and intercept values). The model uses predefined slope and intercept parameters that are calculated from luma and chroma sample values, enabling efficient chroma block reconstruction. This parameter-based approach reduces the need for complex computational operations while maintaining data efficiency improvements.
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.