Video Coding Quantiser Index Estimation
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Solution Overview
Problem
Existing video coding methods struggle to maintain constant video quality across different types of pictures, as they do not effectively account for masking effects and variations in quantiser indices, leading to deviations from target quality parameters.
Innovation Solution
A video coding method that uses a quadratic model to estimate the quantiser index (QP) based on a perceptual quality measure, incorporating a masking term and contrast measures, and applies corrections to account for changes in masking effects due to quantiser index variations, ensuring consistent quality by selecting appropriate previously coded and decoded pictures for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing video coding methods are used, then video coding can be performed, but constant video quality cannot be maintained across different picture types due to ineffective accounting for masking effects and quantiser index variations
Solution Approach 1:
The patent applies parameter changes by using a quadratic model to estimate the quantiser index based on perceptual quality measures and masking terms. The model dynamically adjusts the quantiser index (QP) according to picture type, complexity, and masking effects, rather than using fixed or simple linear relationships. This allows the coding system to adapt parameters to maintain constant quality across different picture types while accurately predicting the relationship between QP and perceptual quality.
2Device complexity
If quantiser index variations are not properly accounted for, then coding process is simpler, but deviations from target quality parameters occur
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the quadratic model parameters (a, b, c coefficients) for different picture types and complexity levels before actual video coding. The model relationships between quantiser index, perceptual quality, and masking terms are established in advance, allowing the coding system to quickly apply corrections without complex real-time calculations. This preparatory work enables accurate quality control while keeping the actual coding process relatively simple.
Data Source
AI summary
Pictures are coded using a coding algorithm with a variable parameter QP so that the quality of the coding varies. First (100), a target value TiMOS is specified for a quality measurement parameter. Then, for each picture (or part of a picture) to be coded, one estimates, independently of the other pictures, a value for the variable parameter QP based on a) the target value for that picture area and b) a masking measure C that depends on the picture content of that picture area. The picture is then coded (112) using the estimated value. The masking measure may be compensated (108, 122) to allow for the effect of the coding quality upon the masking effect.