Vector Segmentation for Stable Positional Coding
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Solution Overview
Problem
Conventional methods for positional coding of audio/video signals face inefficiencies due to large codeword indices and varying energy content across input vector segments, leading to complex and inefficient processing.
Innovation Solution
A method for non-recursive segmentation of input vectors into equal-sized segments, followed by recursive determination of relative energy differences between segments, allowing for efficient coding by distributing bits based on energy and segment length.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional clustering is used to reduce complexity, then device complexity is reduced, but manufacturing precision deteriorates because different parts of the input vector have very different sizes making positional coding inefficient
Solution Approach 1:
The input vector is divided into multiple segments of equal length along its spatial dimension. This segmentation allows each segment to be processed with uniform coding parameters, improving coding efficiency while maintaining manageable complexity through localized processing of smaller vector portions
Solution Approach 2:
Different coding parameters are applied to different segments based on their local energy characteristics. Each segment undergoes independent energy normalization and positional coding with parameters optimized for its specific properties, achieving high precision without requiring complex global processing
2Manufacturing precision
If the input vector is segmented into equal-sized segments, then manufacturing precision is improved for positional coding, but device complexity increases due to the need for recursive energy difference determination
Solution Approach 1:
Energy normalization is performed preliminarily on each segment before positional coding. By pre-computing the energy characteristics and normalizing each segment, the subsequent positional coding operates on standardized data, improving efficiency while the preprocessing structure maintains complexity management
Solution Approach 2:
The coding process is structured as periodic operations across segments: segment the vector, compute energy differences, normalize each segment, perform positional coding, and repeat for all segments. This periodic structure breaks down complex processing into manageable, repetitive steps that improve precision without overwhelming complexity
Data Source
AI summary
A method for partitioning of input vectors for coding is presented. The method comprises obtaining of an input vector. The input vector is segmented, in a non-recursive manner, into an integer number, NSEG, of input vector segments. A representation of a respective relative energy difference between parts of the input vector on each side of each boundary between the input vector segments is determined, in a recursive manner. The input vector segments and the representations of the relative energy differences are provided for individual coding. Partitioning units and computer programs for partitioning of input vectors for coding, as well as positional encoders, are presented.