Target Vector Identification With Sorted Distance Pruning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech and audio codecs face challenges in reducing computational complexity while maintaining coding efficiency, particularly in identifying the nearest code vector in structured codebooks, which is crucial for low-complexity mobile terminal communications.
Innovation Solution
The method involves identifying target vectors from candidate vectors by transforming the input vector into a sorted absolute-valued representation and using a reference vector to efficiently compute distances, reducing the need for explicit distance calculations by leveraging the triangle inequality, thereby minimizing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit distance calculations are performed for all candidate vectors to identify the nearest code vector, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent transforms the input vector into a sorted absolute-valued representation before distance calculations. This preliminary transformation organizes the data in a way that enables efficient distance computation using the triangle inequality, reducing the need for full explicit distance calculations while maintaining accuracy in identifying the nearest code vector
Solution Approach 2:
The patent extracts and utilizes only the necessary components for distance calculation by working with sorted absolute values rather than full vector components. This extraction approach allows the algorithm to compute distances using only relevant information, significantly reducing computational complexity while preserving measurement precision
2Device complexity
If structured codebooks are used to reduce storage and complexity, then device complexity is reduced, but coding efficiency may be compromised
Solution Approach 1:
The patent changes the parameter representation by transforming input vectors into sorted absolute-valued forms. This parameter transformation enables the use of structured codebooks with lattice structures while maintaining coding efficiency, as the transformed parameters allow for more efficient search and distance calculation algorithms that work well with structured codebook organizations
Data Source
Figure 1~3
Figure 4
Figure 5
AI summary
It is inter alia disclosed to identify one or more target vectors from a plurality of candidate vectors, each candidate vector having sorted elements and being associated with a respective class of one or more code vectors of a codebook and at least one of the candidate vectors being associated with a respective class of two or more code vectors that comprise the respective candidate vector and at least one code vector obtainable from the respective candidate vector by one of permutation and signed permutation, the target vectors having, among all candidate vectors of the plurality of candidate vectors, smallest distances towards a at least sorted representation of an input vector. The identifying comprises checking, for a candidate vector of the plurality of candidate vectors, at least based on a distance between the candidate vector and a reference vector and on a distance between the reference vector and the at least sorted representation of the input vector, if a distance between the at least sorted representation of the input vector and the candidate vector is larger than a distance between the at least sorted representation of the input vector and the reference vector. The identifying further comprises computing, for the candidate vector, the distance between the at least sorted representation of the input vector and the candidate vector only if the checking yields a negative result.