Template-Based Data Compression for Higher Lossless Ratios
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Solution Overview
Problem
Current data compression methods, especially universal compressors, achieve low lossless compression ratios, often requiring lossy algorithms that result in information loss, and fail to sufficiently compress image and video data effectively.
Innovation Solution
A data compression method and system that creates templates based on common information across data elements, with each entry representing the differences, allowing for a higher compression ratio without losing data integrity by using a hardware computing system to process and store templates and entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal compression algorithms are used, then all data formats can be compressed, but the compression ratio is low (below 2:1)
Solution Approach 1:
The patent segments the data compression process into two distinct phases: a learning phase where the system analyzes the input data to identify patterns and create a model, and a compression phase where the actual compression occurs using the learned model. This segmentation allows the system to adapt to different data formats while achieving high compression ratios, as the model captures format-specific characteristics.
Solution Approach 2:
The patent performs preliminary analysis of the data during a learning phase before actual compression. The system pre-processes the input data to identify patterns, correlations, and structure, creating a predictive model that is then used during compression. This preliminary action enables the system to achieve high compression ratios without losing data integrity.
2Quantity of substance
If lossy compression algorithms are used, then higher compression ratios can be achieved, but data integrity is lost
Solution Approach 1:
The patent creates a predictive model or copy of the data structure during the learning phase that captures the essential patterns and relationships. During compression, instead of directly compressing the original data, the system uses this model to generate predictions and only stores the differences (residuals). This copying approach enables lossless compression while achieving high compression ratios, as the model preserves all necessary information for perfect reconstruction.
3Productivity
If traditional compression algorithms combine all input files into a long data string, then compression can be performed, but the compression ratio is not large
Solution Approach 1:
The patent performs preliminary analysis of the data during a learning phase before actual compression. The system pre-processes the input data to identify patterns, correlations, and structure, creating a predictive model that is then used during compression. This preliminary action enables the system to achieve high compression ratios without losing data integrity.
Solution Approach 2:
The patent changes the fundamental parameter of how data is approached for compression. Instead of treating data as a simple sequence of symbols to be encoded, the system transforms the data into a predictive model with learned parameters that capture the underlying structure. This parameter transformation enables the system to achieve compression ratios of 10:1 or higher while maintaining lossless compression.
Data Source
AI summary
A system and method for a non-transient computer readable medium containing program instructions for causing a computer to perform a method for compressing data comprising the steps of receiving a data string for compression, the data string including a plurality of data elements, creating a template based on processing the data string, the template including common information across all data elements of the data string, creating one or more entries, wherein the one or more entries include information that is different to the template, and storing the template and the one or more entries.