Input Vector Segmentation for Positional Coding Across Uneven Energy
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Solution Overview
Problem
Conventional positional coding schemes for 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 positional coding by distributing bits based on energy and segment lengths.
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, where each segment is processed separately through clustering and positional coding. This segmentation ensures that each segment maintains consistent dimensions, improving coding efficiency while keeping overall complexity manageable through parallel processing of smaller segments
Solution Approach 2:
Different clustering parameters and codebooks are applied to different segments based on their local characteristics. Each segment undergoes independent processing with optimized parameters tailored to its specific energy distribution and content, thereby improving local coding precision without compromising overall system complexity
2Device complexity
If no pre-processing is applied to the input vector, then device complexity remains low, but manufacturing precision deteriorates due to large codeword indices and inefficient positional coding
Solution Approach 1:
The input vector undergoes preliminary segmentation into equal-length segments before the main positional coding process. This pre-processing step organizes the data structure in advance, ensuring that subsequent clustering and coding operations work with uniformly sized segments, thereby improving coding precision without requiring complex real-time adjustments
Solution Approach 2:
By dividing the input vector into multiple smaller segments of equal length, the system reduces the dimensionality of each coding operation. This segmentation allows positional coding to work more efficiently on smaller, manageable segments rather than attempting to code the entire high-dimensional vector at once, improving precision while maintaining reasonable 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.