Vector Quantizer Search Using Centroid-Sorted Codebook Classes
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Solution Overview
Problem
Vector quantization systems face challenges in balancing computational complexity and accuracy, especially when quantizing multiple vectors simultaneously, as existing optimization techniques struggle to maintain quality and limit complexity constraints.
Innovation Solution
The system divides the codebook into classes and sorts codevectors based on centroids, allowing for a dynamic adaptation of the search space based on the number of input vectors, reducing computational complexity while maintaining high accuracy by searching the most probable candidate codevectors first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large codebook is used to improve quantization accuracy, then the accuracy is improved, but the computational complexity increases
Solution Approach 1:
The codebook is divided into multiple classes, each representing a subset of codevectors. This segmentation allows the search to be restricted to relevant classes only, reducing the effective search space while maintaining high accuracy. The patent applies this by partitioning the full codebook into class-specific subsets that can be searched independently based on input characteristics.
Solution Approach 2:
Centroids are pre-computed for each codebook class to enable rapid classification of input vectors. This preliminary action allows the system to determine which codebook class an input vector belongs to before the actual quantization search, significantly reducing the computational complexity of the full search while maintaining accuracy.
2Device complexity
If the codebook search is optimized for a fixed number of vectors, then the complexity is reduced for that specific case, but the system lacks adaptability to varying numbers of input vectors
Solution Approach 1:
The system dynamically adapts the codebook search strategy based on the number of input vectors. When multiple vectors are to be quantized, the patent utilizes class-specific codebook subsets and centroids to reduce the search space proportionally. This dynamic adaptation maintains low computational complexity across varying input sizes while preserving quantization accuracy.
Solution Approach 2:
The search parameters (codebook subset size, centroid usage) are changed based on the number of input vectors. The system adjusts which codebook classes are searched and how centroids are utilized, transforming the fixed-optimization approach into a flexible, parameter-adaptive system that maintains efficiency across different operating conditions.
Data Source
AI summary
Vector Quantizer and method therein for vector quantization, e.g. in a transform audio codec. The method comprises comparing an input target vector with four centroids C0, C1, C0,flip and C1,flip, wherein centroid C0,flip is a flipped version of centroid C0 and centroid C1,flip is a flipped version of centroid C1, each centroid representing a respective class of codevectors. A starting point for a search related to the input target vector in the codebook is determined, based on the comparison. A search is performed in the codebook, starting at the determined starting point, and a codevector is identified to represent the input target vector. A number of input target vectors per block or time segment is variable. A search space is dynamically adjusted to the number of input target vectors. The codevectors are sorted according to a distortion measure reflecting the distance between each codevector and the centroids C0 and C1.