Variable-Bit-Rate LPC Filter Quantization With Multi-Reference Residual Coding
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Solution Overview
Problem
Current speech and audio coding techniques, such as CELP, face challenges in efficiently representing LPC filters at variable bit rates while maintaining subjective quality, especially in applications requiring low bit rates for mixed content like multimedia streaming and broadcast, where fixed bit rates and separate quantization in each frame lead to inefficiencies.
Innovation Solution
A device and method for quantizing LPC filters using a multi-reference differential quantization scheme, where LPC filters are differentially quantized relative to past or future filters or their interpolated/extrapolated versions, selecting the reference that provides the lowest distortion at a given bit rate or the lowest bit rate for a given distortion level, and employing a variable bit rate quantizer to optimize bit allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed bit rate quantization is used for LPC filters in each frame, then device complexity is reduced, but bit rate efficiency and quality deteriorate
Solution Approach 1:
The patent applies dynamics by transitioning from fixed bit rate quantization to variable bit rate quantization. The quantization device dynamically adjusts the number of bits allocated to LPC filter quantization based on the complexity of the filter coefficients and the desired quality level. This allows the system to use more bits when high accuracy is needed and fewer bits when the filter is simpler, thereby improving bit rate efficiency while maintaining or enhancing quantization accuracy compared to fixed approaches.
Solution Approach 2:
The patent changes the parameter of bit allocation from fixed to variable. By introducing variable bit rate quantization, the system can adaptively modify the quantization precision according to the actual needs of each frame or super-frame. This parameter change enables better trade-off between bit rate consumption and LPC filter reconstruction quality, resolving the contradiction between device complexity and quantization accuracy.
2Adaptability or versatility
If separate quantization is performed in each frame, then adaptability to local variations is improved, but bit rate efficiency deteriorates
Solution Approach 1:
The patent merges the quantization process across multiple frames by introducing super-frame based quantization. Instead of independently quantizing each frame, the system groups frames into super-frames and performs joint quantization. This merging approach exploits temporal correlations between adjacent frames, allowing the use of differential quantization where only the differences between frames are encoded. This significantly improves bit rate efficiency while maintaining adaptability to local variations through the differential encoding mechanism.
Solution Approach 2:
The patent applies preliminary action by performing quantization of reference frames or super-frames before the actual frames that depend on them. In the differential quantization scheme, the reference super-frame is quantized first, and subsequent frames are quantized relative to this reference. This preliminary quantization enables more efficient bit rate allocation by capturing the essential information in the reference and only encoding variations in the subsequent frames, thereby improving overall bit rate efficiency while preserving adaptability.
3Loss of information
If variable bit rate quantization is used, then bit rate efficiency is improved, but device complexity increases
Solution Approach 1:
The patent manages device complexity by implementing variable bit rate quantization through parameter changes in a structured manner. The system varies the quantization precision and bit allocation parameters based on predefined criteria and frame characteristics, rather than requiring complex real-time optimization algorithms. This approach achieves improved bit rate efficiency through adaptive parameter adjustment while keeping the device complexity manageable by using rule-based parameter selection rather than computationally intensive optimization.
Data Source
AI summary
A device and a method for quantizing a LPC filter in the form of an input vector in a quantization domain, comprises a calculator of a first-stage approximation of the input vector, a subtractor of the first-stage approximation from the input vector to produce a residual vector, a calculator of a weighting function from the first-stage approximation, a warper of the residual vector with the weighting function, and a quantizer of the weighted residual vector to supply a quantized weighted residual vector. A device and a method for inverse quantizing of a LPC filter, comprises means for receiving coded indices representative of a first-stage approximation of a vector representative of the LPC filter in a quantization domain and of a quantized weighted residual version of the vector, a calculator of an inverse weighting function from the first-stage approximation, an inverse quantizer of the quantized weighted residual version of the vector to produce a weighted residual vector, a multiplier of the weighted residual vector by the inverse weighting function to produce a residual vector, and an adder of the first-stage approximation with the residual vector to produce the vector representative of the LPC filter in the quantization domain.