Chroma Prediction Linear Modeling With Fewer Reference Samples
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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 impacting overall video coding and decoding efficiency.
Innovation Solution
A method and device for video component prediction that involves selecting multiple first picture component reference values, performing filtering processing, determining to-be-predicted picture component reference values, and constructing a component linear model to reduce complexity and improve prediction efficiency, using a video component prediction device with a processor and memory to execute these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If down-sampling processing is performed using sample values in a luma neighbouring region to construct a linear model, then chroma prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the necessary reference samples from the luma neighbouring region rather than processing all available samples. Specifically, it selects reference samples from the top and left neighbouring blocks that are most relevant for predicting the current chroma block, thereby reducing the number of samples requiring down-sampling and linear model construction while maintaining prediction accuracy.
Solution Approach 2:
The patent segments the luma neighbouring region into distinct reference blocks and selectively processes only those blocks that contain valid reference samples. By dividing the neighbouring region into manageable segments and processing only the relevant ones, the computational load is reduced while still capturing the essential spatial correlations needed for accurate chroma prediction.
2Measurement precision
If a large number of neighbouring reference blocks are used to construct the linear model, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by using only a subset of available reference blocks rather than all neighbouring blocks. It selectively identifies and processes reference blocks that are most likely to contribute to accurate prediction, avoiding the time-consuming processing of redundant or less relevant blocks while maintaining sufficient prediction accuracy.
Solution Approach 2:
The patent performs preliminary identification and selection of reference blocks before constructing the linear model. By pre-selecting which reference blocks to use based on their validity and relevance, the system avoids unnecessary processing during the model construction phase, thereby reducing overall processing time while preserving prediction accuracy.
3Reliability
If filtering processing is performed on all reference samples, then model robustness is improved, but computational workload increases
Solution Approach 1:
The patent extracts only the essential reference samples that require filtering for robust model construction. By identifying and processing only those samples that significantly contribute to model reliability, the filtering workload is reduced while maintaining the robustness needed for accurate chroma prediction under varying content conditions.
Solution Approach 2:
The patent applies filtering selectively to specific reference samples based on their local characteristics and importance. Rather than uniformly filtering all reference samples, it focuses filtering operations on samples that are most critical for model robustness, thereby reducing overall computational workload while preserving model reliability where it matters most.
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.