Cross-Component Video Prediction With Edge-Based Sample Selection
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Solution Overview
Problem
The existing video coding standards, such as Versatile Video Coding (VVC/H.266), face delays in the coding pipeline due to the need to calculate mean values for cross-component prediction models using a large number of reference samples, which affects the efficiency of data compression.
Innovation Solution
A method is introduced to reduce delays by determining a subset of samples within a reference area for classifier parameters in cross-component prediction models, using one, two, three, or four samples, and applying edge detection mechanisms to derive multiple prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of reference samples are used to calculate mean values for cross-component prediction models, then prediction accuracy is improved, but coding pipeline delay increases
Solution Approach 1:
The patent extracts only the necessary subset of reference samples from the full reference area, selecting specific samples based on edge detection results rather than using all available samples. This reduces the calculation burden while maintaining prediction accuracy by focusing on the most relevant samples for the given block characteristics.
Solution Approach 2:
The patent segments the reference area into different regions based on edge detection, identifying significant edges and selecting samples from specific segments. This segmentation allows the system to process only relevant portions of the reference area, reducing overall computation time while preserving important prediction information.
2Reliability
If mean values of all reference samples are calculated for classifier parameters, then prediction reliability is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by selecting different samples for calculation based on the local characteristics of the current block, specifically using edge detection to identify which regions contain important information. This allows the system to maintain high prediction reliability for blocks with significant edges while reducing complexity for blocks without edges.
Solution Approach 2:
Instead of calculating mean values for all reference samples (excessive action), the patent performs partial action by calculating mean values only for the selected subset of samples that are most relevant to the current block's characteristics, as determined by edge detection. This reduces processing complexity while maintaining sufficient prediction reliability.
3Manufacturing precision
If more reference samples are used in the template area, then model parameter accuracy is improved, but coding speed decreases
Solution Approach 1:
The patent performs preliminary edge detection on the reference area before selecting samples for mean value calculation. This preliminary action identifies which regions contain important edge information, allowing the system to pre-determine the subset of samples that will be used, thereby maintaining model parameter accuracy while reducing the number of calculations needed.
Solution Approach 2:
The patent changes the parameter of sample selection from using all reference samples to using a dynamically determined subset based on edge detection results. This parameter change allows the system to adapt the number of samples used based on the actual content characteristics, optimizing the balance between model parameter accuracy and coding speed for different types of video content.
Data Source
AI summary
A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel (1200); reconstructing samples of said luminance channels of the image block unit (1202); determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more of reference samples in a neighboring block in current color channel/frame, in the neighboring of a co-located block in reference color channel/frame (1204); and/or inside the co-located block in reference color channel/frame; determining a subset of samples in said reference area to be used for calculating classifier parameters of a cross-component prediction model (1206); and predicting said target samples of at least one color channel of the image block unit using the cross-component prediction model based on reconstructed samples in the reference color channel/frame of the reference area (1208).