PVQ Shape Search With Dynamic Precision for Fixed-Point DSPs
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Solution Overview
Problem
Structured Pyramid Vector Quantization (PVQ) for speech and audio coding faces challenges in achieving computationally efficient shape search while maintaining high Signal to Noise Ratio (SNR), especially in implementations with fixed precision Digital Signal Processors (DSPs), particularly for high-rate coding where the number of unit pulses and dimensions are high.
Innovation Solution
The method involves iteratively adding unit pulses in an inner dimension search loop, determining possible upshifts in bit word length based on maximum absolute values and accumulated correlation, and adapting precision dynamically within the search loop to optimize bit word length usage, ensuring efficient calculations without losing significant correlation information.
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 computational complexity and memory requirements increase significantly
Solution Approach 1:
The patent segments the vector quantization process into structured components (pyramid structure with hierarchical levels) that can be processed separately and efficiently. By dividing the N-dimensional space into a pyramid structure with manageable levels, the computational complexity is reduced while maintaining quantization quality.
Solution Approach 2:
The patent implements dynamic search strategies that adapt to the specific characteristics of the input signal. The search procedure dynamically adjusts its behavior based on signal properties, enabling efficient computation while maintaining high SNR performance for different types of speech and audio signals.
2Measurement precision
If higher rate speech and audio coding is used to improve quality, then the number of unit pulses and dimensions increase, but search complexity increases significantly
Solution Approach 1:
The patent divides the high-dimensional search space into a pyramid structure with multiple hierarchical levels. This segmentation allows the search to proceed through manageable stages rather than searching the entire high-dimensional space at once, reducing search complexity while maintaining the ability to represent high-rate coding with many unit pulses.
Solution Approach 2:
The patent transforms the high-dimensional vector quantization problem into a hierarchical pyramid structure that adds a temporal/directional dimension to the search process. By organizing the search in a pyramid hierarchy rather than a flat high-dimensional space, the complexity of searching through many dimensions is managed more efficiently.
3Device complexity
If fixed precision DSP is used to reduce hardware requirements, then memory and computational resources are reduced, but precision and SNR performance deteriorate
Solution Approach 1:
The patent implements dynamic precision management that adapts to the specific requirements of each processing stage and signal characteristic. The fixed-precision DSP operates with optimized precision levels that are dynamically selected based on the pyramid level and signal properties, preventing precision deterioration while maintaining hardware efficiency.
Solution Approach 2:
The patent changes the parameters of the quantization process to be compatible with fixed-precision arithmetic. By adjusting the pyramid structure parameters, search thresholds, and correlation calculation methods to work within fixed precision constraints, the system maintains SNR performance without requiring variable precision hardware.
4Device complexity
If structured PVQ is used to reduce complexity compared to unconstrained VQ, then computational requirements are reduced, but quantization performance and SNR may deteriorate
Solution Approach 1:
The patent segments the quantization process into a pyramid structure where each level handles specific aspects of the vector representation. This segmentation allows structured PVQ to maintain computational efficiency while preserving quantization performance by carefully designing the hierarchical decomposition to capture the essential signal characteristics.
Solution Approach 2:
The patent incorporates feedback mechanisms in the pyramid search process where correlation values and search results from previous levels inform subsequent search decisions. This feedback allows the structured PVQ to adapt and improve quantization performance at each hierarchical level while maintaining computational efficiency.
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, maxampy, of a current vector y, whether more than a current bit word length is needed to represent enloopy, in a lossless manner in the upcoming inner dimension loop. The variable enloopy 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.