Sequential Weight Encoding for Higher-Ratio Data Compression
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Solution Overview
Problem
Current data compression methods are inefficient in reducing storage and transmission requirements, particularly for datasets with repetitive patterns, as they fail to effectively utilize the sequential location of data elements to assign weights for optimal compression.
Innovation Solution
A weight function is applied to data elements in a dataset based on their sequential location, calculating increasing or decreasing weights to dynamically adjust encoding, using statistical encoders like adaptive arithmetic coding or Huffman coding, to create a compressed dataset with reduced storage size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods (Huffman coding, Arithmetic coding) are used, then compression is achieved, but they fail to effectively utilize sequential location of data elements to assign weights, resulting in suboptimal compression performance
Solution Approach 1:
The patent applies dynamics by making the weight assignment adaptive rather than static. The weight function dynamically adjusts weights based on the sequential location of data elements, allowing the compression algorithm to adapt to local patterns in the data. This is achieved through calculating weights as a function of previously processed sequential locations, enabling the system to respond to changing data characteristics during compression.
Solution Approach 2:
The patent implements local quality by assigning different weights to different data elements based on their specific sequential locations and contexts. Rather than applying a uniform compression strategy throughout the entire dataset, the weight function creates location-specific weight assignments that reflect local data patterns. This allows optimal compression parameters to be tailored to each position in the sequence.
2Productivity
If weight function calculates increasing values for previously processed locations, then better compression is achieved for certain patterns, but calculation complexity increases
Solution Approach 1:
The patent applies partial action by focusing the weight calculation on a limited context window of previously processed locations rather than the entire history of data elements. The weight function considers only the necessary prior locations to capture relevant patterns, avoiding unnecessary computations. This selective approach achieves sufficient compression performance without requiring excessive computational resources.
3Productivity
If weight function calculates decreasing values for previously processed locations, then recent patterns are emphasized, but loss of information from earlier locations occurs
Solution Approach 1:
The patent applies parameter changes by using a decreasing weight function that systematically adjusts the influence of historical locations. Rather than treating all previous locations equally or using fixed weights, the parameter changes in the weight function allow recent patterns to be emphasized while maintaining a controlled, systematic reduction of weight for earlier locations. This ensures information is prioritized by recency in a mathematically defined manner.
Data Source
AI summary
There is provided a computer implemented method of compressing a baseline dataset comprising a sequence of a plurality of instances of a plurality of unique data elements, the method comprising: providing a weight function that calculates an increasing value for a weight for each one of the plurality of instances of each one of the plurality of unique data elements in the baseline dataset, as a function of increasing number of previously processed sequential locations of each of the plurality of instances of each respective unique data element within the baseline dataset relative to a current sequential location of the baseline dataset, computing an encoding for the baseline dataset according to a distribution of the weight function computed for the plurality of unique data elements in the baseline dataset, and creating a compressed dataset according to the encoding.