Cross-Component Color Prediction Using Filtered Reference Pixels
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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 to calculate the model parameter for cross-component prediction processing.
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 reference pixel subset. By identifying and removing unimportant or exceptional reference pixels, the method derives model parameters using a reduced subset that maintains prediction accuracy while significantly lowering calculation complexity and memory bandwidth requirements
Solution Approach 2:
The patent changes the parameter of reference pixel quantity from a large comprehensive set to a reduced subset. By modifying the selection criteria and quantity of reference pixels used in model parameter derivation, the method achieves a balance between prediction reliability and computational efficiency
2Quantity of substance
If a large number of samples are used to construct the prediction model, then more comprehensive data is available, but exceptional samples reduce the model accuracy
Solution Approach 1:
The patent extracts and removes exceptional reference pixels from the reference pixel set that would negatively impact model accuracy. By identifying and excluding these problematic samples, the method ensures that the remaining reference pixel subset contains only high-quality data for accurate model parameter derivation
Solution Approach 2:
The patent applies different quality standards to different reference pixels. By evaluating each reference pixel's contribution and quality, the method selectively includes only those pixels that meet the accuracy requirements, ensuring local quality optimization in the reference pixel subset
3Reliability
If all reference pixels are used for prediction, then the prediction model is more comprehensive, but the processing time and computational load increase
Solution Approach 1:
The patent extracts only the essential reference pixels needed for effective prediction by forming a reference pixel subset. This extraction process removes redundant and unimportant pixels, maintaining prediction completeness while significantly improving prediction efficiency through reduced computational load
Solution Approach 2:
The patent applies partial action by using a subset of reference pixels rather than the complete set. This partial approach is sufficient to achieve accurate prediction results while avoiding the excessive computational requirements of using all available reference pixels
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.