Pyramid Vector Quantizer Shape Search for Adaptive Bit-Width Control
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Solution Overview
Problem
Structured Pyramid Vector Quantization (PVQ) for speech and audio coding faces challenges in achieving efficient search methods that maintain high Signal to Noise Ratio (SNR) while managing complexity, especially in high-rate coding scenarios with large dimensions and unit pulses, particularly in fixed precision Digital Signal Processors (DSPs).
Innovation Solution
The method involves determining the need for a longer bit word length based on the maximum pulse amplitude and accumulated energy during the PVQ shape search, adapting the bit word length for inner loop calculations to ensure lossless representation and optimizing the search process by employing different precision levels dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unconstrained vector quantization is used to achieve optimal quantization performance, then Signal to Noise Ratio is improved, but complexity and memory capacity requirements increase significantly
Solution Approach 1:
The patent segments the quantization process into structured components (pyramid structure with hierarchical levels) rather than treating it as an unconstrained optimization problem. This segmentation allows the system to achieve good SNR performance while maintaining manageable complexity through organized search procedures at different hierarchical levels.
Solution Approach 2:
The patent employs dynamic search procedures that adapt the search strategy based on the current state of the quantization process. The encoder dynamically adjusts the search depth and precision at different stages, enabling efficient exploration of the quantization space without requiring exhaustive search, thus balancing SNR performance with computational complexity.
2Measurement precision
If higher precision bit word length is used to maintain lossless representation of accumulated energy, then measurement precision is improved, but computational complexity and memory usage increase
Solution Approach 1:
The patent dynamically changes the precision parameter (bit word length) based on the actual needs of the quantization process. Instead of using fixed high precision throughout, the system adjusts the bit word length for representing accumulated energy according to the current search stage and requirements, achieving lossless representation where needed while reducing precision where sufficient, thus optimizing the trade-off between precision and complexity.
Solution Approach 2:
The patent applies partial precision action by using higher precision bit word lengths only when necessary for maintaining lossless representation of accumulated energy, rather than applying maximum precision uniformly throughout the entire process. This selective application of precision reduces overall computational complexity while maintaining accuracy where critical.
Data Source
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AI summary
An encoder and a method therein for Pyramid Vector Quantizer, PVQ, shape search, the PVQ taking a target vector x as input and deriving a vector y by iteratively adding unit pulses in an inner dimension search loop. The method comprises, before entering a next inner dimension search loop for unit pulse addition, determining, based on the maximum pulse amplitude, maxamp y , of a current vector y, whether more than a current bit word length is needed to represent enloop y , in a lossless manner in the upcoming inner dimension loop. The variable enloop y is related to an accumulated energy of the vector y. The performing of this method enables the encoder to keep the complexity of the search at a reasonable level. For example, it enables the encoder to apply an increased precision loop only when it may be needed, by analyzing whether the "worst case scenario" in the upcoming inner loop would require an inner loop with a higher precision than the one currently used.