Cross-Component Colour Prediction with Filtered Reference Pixels
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Solution Overview
Problem
The existing methods for predicting color components in video coding, particularly in the H.266/Versatile Video Coding standard, suffer from high computational complexity and memory bandwidth due to the large number of samples used for model parameter derivation, which can lead to inaccurate prediction models when abnormal samples are present.
Innovation Solution
A method is introduced to reduce the number of adjacent reference pixels by constructing a subset of these pixels, using them to calculate model parameters for multiple prediction sub-models, thereby reducing computational complexity and memory bandwidth while improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of samples are used for model parameter derivation, then the prediction model can be more comprehensive, but the computational complexity and memory bandwidth increase significantly
Solution Approach 1:
The patent extracts only the necessary and effective reference pixels from the complete set of adjacent reference pixels. By identifying and removing abnormal or unimportant samples, the method derives model parameters using a reduced subset of pixels that maintains prediction accuracy while significantly lowering computational complexity and memory bandwidth requirements.
2Reliability
If a large number of samples are used for model parameter derivation, then the prediction model can be more comprehensive, but the memory bandwidth consumption increases
Solution Approach 1:
The method extracts a reduced subset of reference pixels from the complete adjacent reference pixel set by identifying and excluding abnormal or unimportant samples. This extraction process maintains the essential information needed for accurate prediction while reducing the quantity of data that needs to be stored and processed, thereby decreasing memory bandwidth consumption.
3Adaptability or versatility
If all adjacent reference pixels are used for prediction, then the prediction model covers more information, but abnormal samples reduce prediction accuracy
Solution Approach 1:
The patent applies the extraction principle by identifying and removing abnormal or unimportant reference pixels from the complete set of adjacent reference pixels. This selective extraction maintains the comprehensive coverage of valid information while eliminating samples that would degrade prediction accuracy, thus resolving the contradiction between model coverage and prediction precision.
Data Source
AI summary
Disclosed are a colour component prediction method, an encoder, a decoder, and a storage medium. The method includes: determining adjacent reference pixels of a current block in a picture; constructing a subset of adjacent reference pixels according to the adjacent reference pixels, wherein the subset of adjacent reference pixels contains a part of the adjacent reference pixels; and calculating model parameters of a prediction model according to the subset of adjacent reference pixels, wherein the prediction model includes N prediction sub-models, the N prediction sub-models correspond to N groups of model parameters, and the prediction sub-models are used to perform, through corresponding model parameters, cross-component prediction of colour components to be predicted.