Color Component Prediction with Reference Sample Screening
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Solution Overview
Problem
Existing color prediction models in video coding and decoding require a large number of samples for model construction, leading to high calculation complexity and memory bandwidth, and are prone to inaccuracies due to exceptional samples.
Innovation Solution
Reduce the number of pixels in the reference pixel set by screening out unimportant or exceptional reference pixels, using a selected subset for model parameter calculation to improve prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of samples are used to construct the prediction model, then the model parameter derivation becomes more comprehensive, but the calculation complexity and memory bandwidth increase significantly
Solution Approach 1:
The patent extracts only the necessary reference pixels from the full reference pixel set to form a subset. By identifying and removing unimportant or exceptional reference pixels, the method derives model parameters using only the essential samples, thereby reducing calculation complexity while maintaining prediction accuracy.
Solution Approach 2:
Instead of using all available reference pixels, the patent applies partial action by selecting a subset that is sufficient for accurate model parameter derivation. This partial sampling approach avoids the excessive computation required for processing the complete reference pixel set while achieving the necessary prediction reliability.
2Reliability
If a large number of samples are used to construct the prediction model, then more comprehensive data is available for model parameter derivation, but the memory bandwidth requirement increases
Solution Approach 1:
The patent extracts a subset of reference pixels that contains only the essential information needed for model parameter derivation. By removing redundant and exceptional pixels, the method reduces the quantity of data that needs to be stored and accessed in memory, thereby reducing memory bandwidth requirements while preserving prediction accuracy.
3Quantity of substance
If all reference pixels including exceptional samples are used, then the reference pixel set is complete, but the prediction model accuracy decreases due to exceptional samples
Solution Approach 1:
The patent extracts a cleaned subset of reference pixels by identifying and removing exceptional samples from the complete reference pixel set. This extraction process eliminates outliers and abnormal pixels that would otherwise degrade the accuracy of the prediction model, while retaining sufficient data for reliable model parameter derivation.
Solution Approach 2:
The patent changes the composition parameter of the reference pixel set by selectively including or excluding specific pixels based on their quality. By transforming the complete set into a filtered subset with improved quality characteristics, the method achieves higher prediction model accuracy without sacrificing the essential information needed for model construction.
Data Source
AI summary
Provided are a method for predicting a colour component, an encoder and a decoder. The method includes: a first reference sample set of a colour component to be predicted of a current block is determined; a reference sample subset is determined from the first reference sample set, where the reference sample subset includes one or more candidate samples selected from the first reference sample set; and a model parameter of a prediction model is calculated according to positions of reference samples in the reference sample subset, where the prediction model is configured to perform prediction processing on the colour component to be predicted of the current block.