Adaptive Quantization for Hybrid Video Coding Efficiency
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Solution Overview
Problem
Current hybrid video coding methods are inefficient for video signals with low spatial correlation, as they rely on fixed zigzag scanning and quantization schemes that do not optimize data rate and distortion effectively for such cases.
Innovation Solution
A method that calculates quantization efficiency and zero efficiency for each subblock of a macroblock, comparing these to decide whether to maintain or set quantized values to zero, optimizing the quantization process based on rate-distortion costs and adapting between spatial and frequency domains for improved coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed zigzag scanning and quantization schemes are used, then coding simplicity is maintained, but coding efficiency deteriorates for video signals with low spatial correlation
Solution Approach 1:
The patent applies dynamics by making the scanning scheme adaptive rather than fixed. The method dynamically selects between zigzag scanning and diagonal scanning based on the correlation characteristics of prediction error samples. When spatial correlation is high, zigzag scanning is used; when correlation is low, diagonal scanning is selected, allowing the coding system to adapt to different video content characteristics and optimize coding efficiency accordingly
Solution Approach 2:
The patent changes the scanning parameter based on the correlation coefficient of prediction error samples. By calculating the correlation and selecting different scanning patterns (zigzag or diagonal) based on this parameter, the system optimizes the ordering of coefficients for entropy coding, thereby improving compression performance for signals with low spatial correlation while maintaining simplicity through rule-based selection
2Measurement precision
If all quantized coefficients are coded, then measurement precision is maintained, but data rate increases for single quantized coefficients unequal to zero
Solution Approach 1:
The patent introduces a second quantization step that modifies the quantized coefficients based on their importance. By calculating an importance value for each coefficient and applying additional quantization with different step sizes based on this importance, the method reduces the data rate for less important coefficients while maintaining precision for significant coefficients, effectively trading off precision for compression ratio in a controlled manner
Data Source
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AI summary
Method for coding a video signal using hybrid coding, comprising: reducing temporal redundancy by block based motion compensated prediction in order to establish a prediction error signal; performing quantization on samples of the prediction error signal or on coefficients resulting from a transformation of the prediction error signal into the frequency domain to obtain quantized values, representing quantized samples or quantized coefficients respectively; calculating a quantization efficiency for the quantized values; calculating a zero efficiency for a quantization, when the quantized values are set to zero; selecting the higher efficiency; and maintaining the quantized values or setting quantized values to zero, for further proceeding, depending on the selected efficiency.