Dependent Scalar Quantization for Video Coding Efficiency
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Solution Overview
Problem
Existing media signal coding techniques, such as those used in video coding, face challenges in achieving high coding efficiency due to the trade-off between bitrate and quantization distortion when setting quantization parameters.
Innovation Solution
The proposed solution involves a method for coding media signals using dependent scalar quantization, where the set of reconstruction levels for a current sample is selected based on quantization indices from previous samples, allowing for a more efficient use of available quantization levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If quantization is rendered finer to decrease distortion, then manufacturing precision is improved, but bitrate increases
Solution Approach 1:
The quantization process is segmented into multiple stages: initial quantization to generate reconstruction levels, followed by selection of specific reconstruction levels from the quantized values. This segmentation allows the system to achieve finer effective quantization precision without proportionally increasing bitrate, as the selection process exploits statistical properties to reduce the number of bits needed to represent the quantization index.
Solution Approach 2:
The patent changes the parameter representation by introducing a selection mechanism that operates on quantization indices. Instead of directly transmitting full-precision quantization values, the system transmits indices that select from predefined reconstruction level sets. This parameter transformation reduces the effective bitrate while maintaining or improving quantization precision through intelligent level selection based on statistical characteristics.
2Quantity of substance
If quantization is rendered coarser to reduce bitrate, then bitrate is reduced, but quantization distortion increases
Solution Approach 1:
The system incorporates feedback mechanisms where reconstruction levels from previous samples influence the selection of reconstruction levels for current samples. This feedback loop allows the quantization process to adapt to local signal characteristics, maintaining higher precision even with coarser overall quantization. The selection process uses information from previously decoded samples to optimize the current quantization decision, effectively reducing distortion without requiring increased bitrate.
3Productivity
If dependent scalar quantization is used to improve coding efficiency, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple reconstruction level sets before the actual quantization and selection process. These reconstruction level sets are prepared in advance based on statistical properties of the signal, allowing the encoder and decoder to operate with reduced complexity during actual encoding/decoding. The preliminary preparation of reconstruction level tables enables efficient selection without requiring complex real-time calculations, thus improving coding efficiency while limiting the increase in device complexity.
Data Source
AI summary
A method for sequentially decoding, from a data stream, a sequence of samples corresponding to a picture, including entropy decoding a quantization index corresponding to a sample, indicating, based on the quantization index, a reconstruction level set of two reconstruction levels sets, the two reconstruction level sets including: a first reconstruction level set that includes reconstruction levels that are even multiples of a quantization step size, wherein the first reconstruction level set excludes reconstruction levels that are odd multiples of the quantization set size; a second reconstruction level set that includes reconstruction levels that are odd multiples of the quantization step size, and dequantizing the sample using the indicated reconstruction level set. The second reconstruction level set excludes reconstruction levels that are even multiples of the quantization step size.