PARCOR Coefficient Quantization for Audio Compression
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Solution Overview
Problem
Conventional lossy audio coding methods do not effectively minimize the entropy of the linear prediction residual and the code amount, leading to suboptimal compression ratios in lossless coding of PARCOR coefficients.
Innovation Solution
Quantizing PARCOR coefficients with higher precision for larger absolute values to reduce the increase in code amount caused by quantization errors, while maintaining lower precision for smaller values, thereby minimizing entropy and improving compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If linear quantization is used to reduce code amount, then code amount is reduced, but quantization error increases significantly
Solution Approach 1:
The patent applies local quality by differentiating quantization precision based on the magnitude of PARCOR coefficients. Coefficients with larger absolute values receive higher precision quantization while coefficients closer to zero receive lower precision quantization. This is implemented through nonlinear quantization that adapts the number of significant bits retained based on the coefficient's distance from zero, thereby optimizing the balance between code amount and quantization error for each local region of coefficient values.
2Device complexity
If conventional lossy coding methods are used, then coding process is simplified, but compression ratio is suboptimal
Solution Approach 1:
The patent applies dynamics by implementing an adaptive quantization process that dynamically adjusts quantization parameters based on the statistical properties of PARCOR coefficients. The system calculates the optimal number of significant bits to retain for each coefficient based on its magnitude, creating a dynamic quantization strategy that optimizes compression ratio while maintaining manageable process complexity through algorithmic automation.
3Measurement precision
If higher precision quantization is applied to all coefficients, then quantization error is reduced, but code amount increases
Solution Approach 1:
The patent applies partial action by selectively applying high precision quantization only to the subset of PARCOR coefficients that have larger absolute values, while applying lower precision quantization to coefficients closer to zero. This partial application of high precision where most needed optimizes the trade-off between reducing quantization error and minimizing code amount, avoiding the excessive action of applying full precision uniformly to all coefficients.
Data Source
AI summary
On a criterion to minimize the entropy of the linear prediction residual of the input signal used for calculation of the input PARCOR coefficient sequence, PARCOR coefficients with larger absolute values are quantized with higher quantization precisions so as to reduce the increase of the code amount of the linear prediction residual caused by the quantization error of the PARCOR coefficients. If the PARCOR coefficient is represented by a value formed by a predetermined number of bits, the number of effective bits from the most significant bit toward the least significant bit included in the output value increases with the absolute value of the PARCOR coefficient.