Spherical Vector Quantization With Sequential Coordinate Search
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Solution Overview
Problem
Existing methods for spherical vector quantization in spatialized audio coding, such as ambisonics and Directional Audio Coding (DiRAC), face challenges in efficiently representing rotation matrices and direction of arrival (DoA) data, leading to high computational complexity and storage costs due to suboptimal grid structures and exhaustive search methods.
Innovation Solution
A method for spherical vector quantization that involves sequential scalar quantization of n−1 spherical coordinates, using a predefined offset to avoid alignment on meridians, and determining quantization levels based on surface area and total points, optimizing the search for nearest neighbors to reduce complexity and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search methods are used for spherical vector quantization, then quantization precision is improved, but computational complexity increases
Solution Approach 1:
The spherical coordinate system is segmented into multiple quantization levels for each coordinate dimension. Instead of performing exhaustive search over all possible combinations, the method divides the search space into manageable segments (quantization levels) and uses a predefined grid structure to limit the number of candidates, thereby reducing computational complexity while maintaining acceptable quantization precision.
Solution Approach 2:
The method applies different quantization strategies to different regions of the spherical coordinate space. By using a predefined grid structure with specific quantization levels for each coordinate, the method optimizes the search locally around the input point rather than performing global exhaustive search, reducing overall computational complexity while maintaining precision in critical regions.
2Quantity of substance
If predefined grid structures are used for spherical vector quantization, then storage costs are reduced, but quantization performance deteriorates
Solution Approach 1:
The method uses a dynamic quantization approach where the number of quantization levels and the grid structure are adapted based on the input point's characteristics. By determining quantization levels dynamically rather than using a fixed rigid grid, the method maintains storage efficiency while improving quantization performance to match the actual data distribution.
Solution Approach 2:
The method changes the parameters of the quantization grid dynamically. Instead of using a fixed grid with constant spacing, the method adjusts the grid parameters (number of levels, spacing) based on the input spherical coordinates, allowing the predefined grid structure to adapt to different regions of the sphere and maintain high quantization performance with reduced storage requirements.
Data Source
AI summary
A method for encoding an input point on an n-dimensional sphere by encoding n−1 spherical coordinates of said input point. The method includes sequential scalar quantization of the n−1 spherical coordinates in order to obtain at most 2n-2 candidates at the end of the sequential scalar quantization of the n−1 coordinates, and subsequently selecting the best candidate which minimizes a distance between the input point and the at most 2n-2 candidates, and determining the separate quantization indices resulting from the sequential scalar quantization of the spherical coordinates of the best candidate and sequentially encoding the separate quantization indices of the best candidate. A corresponding decoding method, an encoding device and a decoding device are also provided.