Chroma Block Decoding With Nonlinear Luma Prediction Filters
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Solution Overview
Problem
Existing video coding techniques using linear model prediction are inadequate for accurately predicting chroma blocks and result in low decoding efficiency, as they fail to account for the diverse nature of block units in video data.
Innovation Solution
A method and device that utilize non-linear prediction model filters, incorporating luma reconstructed samples to generate luma square and gradient values, which are applied to a prediction model filter to reconstruct chroma blocks, enhancing prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If linear model prediction is used for chroma block reconstruction, then the decoding process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent transforms the prediction approach by changing from linear model parameters to non-linear model parameters (luma square values and luma gradient values). This parameter transformation enables more accurate chroma prediction while maintaining decoding efficiency through the use of pre-calculated values from the luma block.
Solution Approach 2:
The patent introduces adaptive selection of prediction models based on block characteristics. By dynamically choosing between different non-linear prediction models (e.g., when to use luma square values vs. luma gradient values), the system optimizes prediction accuracy for diverse block types while maintaining operational efficiency.
2Productivity
If a single linear model equation is used for all block units, then the decoding process is efficient, but the prediction precision varies for different block types
Solution Approach 1:
The patent segments the prediction approach by dividing chroma prediction into multiple non-linear models based on block characteristics. Different prediction models (using luma square values, luma gradient values, or combinations) are applied to different block types, enabling precise prediction while maintaining overall decoding efficiency through selective application.
Solution Approach 2:
The patent applies different prediction quality levels to different regions based on their characteristics. By using non-linear models with locally adapted parameters (luma square values for some regions, luma gradient values for others), the system achieves high prediction precision where needed while maintaining decoding efficiency in other regions.
Data Source
AI summary
A method of decoding video data performed by an electronic device is provided. The method receives the video data and determines a block unit from a current frame included in the video data. The method further determines a plurality of luma reconstructed samples in a luma block of the block unit based on the video data and determines a prediction model filter of a prediction model mode for a chroma block of the block unit based on the video data. The method then determines a prediction model filter of a prediction model mode for a chroma block of the block unit based on the video data and reconstruct the chroma block of the block unit by applying the plurality of luma square values and the plurality of luma gradient values to the prediction model filter.