Cross-Component Linear Model for Efficient Video Chroma Reconstruction
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Solution Overview
Problem
Existing video encoding technologies face challenges in efficiently encoding and decoding high-definition and ultra-high-definition video data while maintaining image quality, particularly in managing the redundancy within video frames.
Innovation Solution
The implementation of a cross-component linear model for reconstructing chroma blocks during video encoding and decoding, utilizing down-sampled luma samples to predict chroma samples, thereby enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video encoding methods are used, then encoding speed is maintained at acceptable levels, but encoding efficiency and image quality cannot be simultaneously optimized for high-definition and ultra-high-definition video data
Solution Approach 1:
The patent applies parameter changes by introducing a cross-component linear model that establishes a mathematical relationship between luma and chroma components. This model uses parameters such as slope and intercept to predict chroma values from luma values, enabling efficient encoding while maintaining image quality through accurate parameter-based prediction rather than traditional lossy compression
Solution Approach 2:
The patent uses an intermediary approach by introducing a linear model as a mediator between the luma component and chroma component. This linear model acts as a bridge that allows the system to infer chroma information from luma information, reducing the amount of data that needs to be encoded while preserving the relationship between color components and maintaining image quality
2Measurement precision
If more video data is processed to achieve high-definition and ultra-high-definition quality, then image quality is improved, but the amount of data to be encoded increases exponentially
Solution Approach 1:
The patent extracts the chroma component information from the full video data by using the cross-component linear model to predict chroma values from luma values. This extraction allows the system to work with reduced data (only luma values need to be encoded) while maintaining the ability to reconstruct high-quality image data through the linear relationship established by the model
Solution Approach 2:
The patent applies universality by creating a linear model that can be applied across different video data types and resolutions. The same linear model parameters can be used to predict chroma values for various video scenarios, making the system universally applicable to high-definition and ultra-high-definition video encoding without requiring separate processing for each data volume
3Manufacturing precision
If complex prediction models are used to maintain image quality, then encoding accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent simplifies the prediction model by changing the mathematical parameters to a linear relationship (y = mx + b) between luma and chroma components. This linear parameterization provides encoding accuracy comparable to more complex models while significantly reducing computational complexity, as linear calculations are much faster than non-linear or deep learning-based predictions
Solution Approach 2:
The patent uses a simple, lightweight linear model that can be quickly computed and discarded for each pixel, rather than using a complex model that requires extensive computational resources. The linear model parameters are simple enough to be calculated efficiently in real-time during encoding, providing sufficient accuracy without the computational burden of more sophisticated prediction algorithms
Data Source
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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.