Latent-Variable Structured Data Compression With Block Encoding
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Solution Overview
Problem
Existing lossless compression methods for structured and semi-structured data fail to achieve optimal compression rates due to modeling assumptions that are not satisfied by actual data in applications such as numerical analysis, hyperspectral imaging, and quantum chemistry, leading to suboptimal performance.
Innovation Solution
Estimate latent variables associated with rows and columns of the data, partition the data into blocks based on these variables, apply a sequential encoding algorithm to each block, and append a compressed encoding of the latent variables to the block encodings, using spectral clustering and side information for improved compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional lossless compression methods (e.g., Lempel-Ziv, frequency-based entropy encoders) are used on structured data, then the compression process is simple and fast, but the compression rate is suboptimal because the methods assume data follows random vector models with stationarity and ergodicity assumptions that are not satisfied by actual structured data
Solution Approach 1:
The patent segments structured data into blocks based on estimated latent variables, allowing different compression strategies to be applied to different segments. This segmentation enables the method to capture local structures in the data while maintaining overall compression efficiency, resolving the contradiction by breaking down the complex task into manageable pieces that can be processed more effectively
Solution Approach 2:
The patent introduces latent variables as an intermediary representation between the original structured data and the compressed form. These latent variables capture the underlying structure and dependencies in the data, serving as a bridge that enables more effective compression. The intermediary latent representation allows the compression algorithm to achieve better rates without directly modeling the complex data dependencies, thus resolving the contradiction between compression performance and algorithmic simplicity
2Productivity
If latent variable estimation and spectral clustering are applied to improve compression rates, then the compression rate increases by at least 5% compared to traditional encoders, but the computational complexity and processing time increase due to the additional estimation and partitioning steps
Solution Approach 1:
The patent performs latent variable estimation and data partitioning as preliminary actions before the actual compression step. By pre-processing the data to identify structural patterns and group similar elements together, the subsequent compression phase becomes more efficient. This preliminary action resolves the time contradiction by investing computational effort upfront to enable faster and more effective compression later
Solution Approach 2:
The patent employs dynamic partitioning of data based on estimated latent variables, allowing the compression strategy to adapt to the specific structure of the input data. Rather than applying a fixed compression scheme, the method dynamically adjusts the partitioning and compression approach based on the discovered data structure, optimizing the balance between compression rate and processing time for different data types
3Productivity
If data is partitioned into blocks based on latent variables and sequential encoding is applied to each block, then the compression achieves optimality by capturing data structure, but the ease of operation decreases due to the complexity of estimating latent variables and managing multiple blocks
Solution Approach 1:
The patent extracts the essential structural information from structured data by estimating latent variables and separating this structural representation from the actual data compression task. By taking out the structural component into a separate latent variable representation, the method simplifies the compression process while maintaining optimality, as the latent variables capture the essential patterns without requiring complex handling of the full data structure
Data Source
AI summary
A computer-implemented method for compressing structured data or semi-structured data is provided. The method comprises: (a) estimating one or more latent variables associated with rows or columns of the structured data or semi-structured data; (b) partitioning the structured data or semi-structured data in one or more blocks according to the one or more one or more latent variables; (c) applying a sequential encoding algorithm to each of the blocks; and (d) appending a compressed encoding of the one or more latent variables.