Cross-Component Linear Chroma Prediction for Lower Video Data Volume
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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 handling chroma blocks during video data compression and decompression.
Innovation Solution
A cross-component linear model is employed to reconstruct chroma blocks using averaged maximum and minimum luma samples and corresponding chroma samples, generating a linear model to predict chroma samples based on luma samples, thereby improving encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is encoded at high definition or ultra-high definition, then image quality is improved, but data volume increases exponentially
Solution Approach 1:
The patent extracts chroma information from the full chroma block and represents it using a linear model derived from luma samples. By taking out the chroma data and replacing it with a model-based prediction, the patent significantly reduces the amount of data that needs to be encoded while maintaining chroma quality, thus resolving the contradiction between image quality and data volume.
Solution Approach 2:
The patent changes the representation parameters of chroma blocks by introducing a linear model with parameters a and b that relate chroma values to luma values. This parameter transformation allows chroma information to be represented more efficiently using fewer bits, enabling high-definition video encoding without exponential data volume increase.
2Measurement precision
If chroma blocks are encoded with full precision, then chroma quality is maintained, but encoding complexity increases
Solution Approach 1:
The patent transforms the encoding task by changing from direct chroma encoding to luma-based linear model encoding. The chroma block is represented using a linear model with parameters a and b that can be derived from luma samples, significantly simplifying the encoding process while maintaining chroma quality through the relationship between luma and chroma components.
Solution Approach 2:
The patent introduces luma samples as an intermediary to represent chroma information. Instead of encoding chroma blocks directly, the patent uses luma samples to predict chroma values through a linear model, thereby reducing encoding complexity while preserving chroma quality through the intermediary luma-chroma relationship.
3Productivity
If chroma blocks are encoded with reduced precision, then encoding efficiency is improved, but chroma quality deteriorates
Solution Approach 1:
The patent changes the approach by representing chroma blocks through a linear model derived from luma samples. This parameter transformation enables reduced precision encoding of chroma information while maintaining quality, as the linear model captures the essential relationship between luma and chroma components, allowing efficient encoding without significant quality loss.
Solution Approach 2:
The patent creates a simplified representation (copy) of chroma blocks using a linear model based on luma samples. This model copy captures the essential chroma information in a more compact form, enabling reduced precision encoding while maintaining visual quality through the mathematical relationship between luma and chroma components.
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.