Residual Quantization in Video Coding for Compression-Quality Balance
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Solution Overview
Problem
Existing video encoding methods lack efficiency in reducing data size while maintaining visual quality and processing speed, due to sub-optimal quantization and dequantization processes that do not account for the statistical distribution of video data.
Innovation Solution
A method involving downsampling, transforming, and adaptively quantizing residuals based on video content, with bin folding and dequantization offset adjustments, to optimize compression and reconstruction quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional quantization is applied to video data, then data compressibility is improved, but visual quality deteriorates due to loss of important frequency components
Solution Approach 1:
The patent changes the quantization parameters dynamically based on the importance of frequency components. Different quantization matrices are applied to different frequency bands, with less aggressive quantization for important low-frequency components and more aggressive quantization for less important high-frequency components. This resolves the contradiction by adapting the quantization strength to the visual importance of each frequency component.
Solution Approach 2:
The patent applies different quantization strategies to different parts of the frequency spectrum. Important frequency components (low frequencies) receive gentler quantization treatment while less important components (high frequencies) receive stronger compression. This local differentiation of quantization quality resolves the contradiction between overall compression and preservation of visual quality.
2Productivity
If aggressive quantization is used to reduce data size, then compression efficiency is improved, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent dynamically adjusts quantization parameters based on the statistical distribution of video data and the importance of different frequency components. By changing quantization strength adaptively rather than uniformly, the system achieves better compression efficiency while maintaining reconstruction accuracy for important visual information.
Solution Approach 2:
The quantization process becomes dynamic rather than static. The system adapts quantization parameters based on the actual video content being encoded, allowing aggressive quantization where appropriate and conservative quantization where visual quality is critical. This dynamic adaptation resolves the contradiction between compression efficiency and reconstruction accuracy.
3Device complexity
If uniform quantization is applied to all frequency components, then processing complexity is reduced, but visual quality deteriorates due to equal treatment of important and unimportant components
Solution Approach 1:
The patent applies different quantization matrices to different frequency components based on their visual importance. Low-frequency components that are more important to visual quality receive different treatment compared to high-frequency components. This local differentiation improves visual quality without significantly increasing processing complexity, as the different treatment is based on the inherent structure of the transform coefficients.
Data Source
AI summary
A method of decoding an encoded stream into a reconstructed output signal is provided, the method comprising: receiving a first output signal decoded from a first base encoded stream according to a first codec; receiving a level encoded stream; decoding the level encoded stream to obtain a set of residuals; and, combining the set of residuals with the first output signal to generate a reconstructed signal, wherein the decoding the level encoded stream comprises: decoding a set of quantized coefficients from the level encoded stream; dequantizing the set of quantized coefficients.