Matching Pursuits Codebook Masking for Dimensional Complexity
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Solution Overview
Problem
Existing data compression techniques face challenges in efficiently compressing multi-dimensional data due to high computational complexity and bit cost, especially in higher dimensions, which limits their feasibility for practical applications.
Innovation Solution
A method is introduced to reduce the complexity of the codebook by selecting a subset of a full n-dimensional dictionary using a mask, allowing for separable application of one-dimensional bases to form two-dimensional bases, thereby reducing the number of calculations and achieving a beneficial rate/distortion trade-off.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full n-dimensional dictionary is used for data compression, then compression accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the full n-dimensional dictionary into multiple codebooks by applying a mask that selects only certain dimensions. This divides the large dictionary into smaller, manageable codebooks that can be processed separately, reducing computational complexity while maintaining compression accuracy for the selected dimensions.
Solution Approach 2:
The patent extracts only the necessary dimensions from the full n-dimensional dictionary using a mask. By taking out only the relevant dimensions needed for the compression task, the system avoids processing unnecessary data, thereby reducing computational complexity while preserving compression accuracy for the extracted dimensions.
2Reliability
If a full codebook is used for higher dimensional coding, then data integrity is maintained, but bit cost increases
Solution Approach 1:
The patent extracts only the essential dimensions from the full codebook using a mask, removing redundant information. This extraction maintains data integrity for the selected dimensions while reducing the total bit cost by eliminating unnecessary dimensional data from the compression process.
3Device complexity
If a mask is applied to select a subset of dimensions, then computational complexity is reduced, but compression accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by selectively masking certain dimensions based on their importance or relevance to the specific compression task. Rather than uniformly processing all dimensions, the mask enables different dimensions to be treated differently, maintaining high compression accuracy for critical dimensions while reducing complexity for less important ones.
Data Source
AI summary
Briefly, in accordance with one embodiment, an approach to employing Matching Pursuits coding of data is described.