Chroma Component Prediction Using Filtered Reference Sample Selection
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Solution Overview
Problem
The complexity of video component prediction in existing video coding standards, such as H.265/HEVC, is high due to the need for down-sampling and constructing linear models based on numerous neighboring reference blocks, leading to low chroma prediction efficiency and overall video coding and decoding inefficiency.
Innovation Solution
A method and device that reduces complexity by selecting multiple first picture component reference values, performing filtering, and constructing a component linear model to predict the to-be-predicted picture component, thereby reducing the workload of filtering operations and improving prediction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If down-sampling processing is performed using sample values in a luma neighbouring region and maximum and minimum are found from reference samples to construct the linear model, then the chroma prediction can be achieved, but the complexity increases and chroma prediction efficiency decreases
Solution Approach 1:
The patent segments the reference sample set into multiple candidate blocks, selecting only the most relevant blocks for model construction. Instead of processing all neighboring reference blocks, the method divides the reference region into segments and selects representative blocks, reducing the number of samples requiring down-sampling and filtering while maintaining prediction accuracy.
Solution Approach 2:
The patent extracts only the essential reference samples needed for accurate chroma prediction. By identifying and extracting the most relevant luma reference blocks from the neighboring region, the method avoids processing redundant samples, thereby reducing computational complexity while preserving the key information necessary for accurate linear model construction.
2Reliability
If a relatively large number of neighbouring reference blocks are used to construct the linear model, then the model may be more accurate, but the complexity increases resulting in low chroma prediction efficiency
Solution Approach 1:
The patent applies partial action by using only a subset of the available reference blocks for model construction. Instead of utilizing all neighboring reference blocks, the method selectively processes a partial set that provides sufficient prediction reliability, thereby avoiding the excessive computational burden of processing all available samples while maintaining adequate prediction accuracy.
Solution Approach 2:
The patent changes the parameter of reference block quantity from a large number to a optimized subset. By adjusting the number of reference blocks used in the linear model construction, the method finds an optimal balance between prediction reliability and coding efficiency, reducing the parameter of sample quantity to achieve better overall performance.
3Measurement precision
If filtering processing is performed on all sample values corresponding to reference values, then the prediction accuracy improves, but the workload increases
Solution Approach 1:
The patent segments the filtering operation to be applied only to selected reference samples rather than all samples. By dividing the reference sample set and applying filtering only to the most critical samples, the method maintains prediction precision while significantly reducing the time lost to filtering operations.
Solution Approach 2:
The patent extracts only the essential reference samples that require filtering processing. By identifying and extracting the key samples that most influence prediction accuracy, the method avoids performing filtering on redundant samples, thereby reducing the workload and processing time while preserving the necessary prediction precision.
Data Source
AI summary
Provided are a picture component prediction method and a video component prediction device. The method includes: determining multiple reference samples from samples in one or more neighboring lines at a top side of the current block according to four preset positions, or determining multiple reference samples from samples in one or more neighboring columns at a left side of the current block according to four preset positions; determining multiple first picture component reference values according to the determined multiple reference samples; performing first filtering processing on sample values of samples corresponding to the multiple first picture component reference values, respectively; determining a parameter of a component linear model; performing mapping processing on a reconstructed value of the first picture component of the current block according to the component linear model; and determining a predicted value of the to-be-predicted picture component of the current block.