Adaptive Data Compression Using Sequential Weight-Based Codewords
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Solution Overview
Problem
Current data compression methods, such as Huffman coding, are inefficient in adapting to the sequential distribution of data elements within a dataset, leading to suboptimal compression performance.
Innovation Solution
A method that calculates weights for each data element based on its sequential location within the dataset, using a weight function to assign shorter codewords to elements closer to the start, dynamically adjusting codewords to maintain compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Huffman coding is used for data compression, then the compression process is simple and static, but the compression performance is suboptimal because it does not adapt to the sequential distribution of data elements
Solution Approach 1:
The patent applies dynamics by transitioning from static Huffman coding to dynamic adaptive Huffman coding. The algorithm continuously updates the frequency weights of data elements as it processes the dataset sequentially, adjusting codeword assignments in real-time based on the current state of the dataset. This dynamic adaptation allows the compression performance to improve as more data is processed, resolving the contradiction between simple static compression and optimal adaptive compression.
Solution Approach 2:
The patent changes the parameter of codeword length based on the sequential position and frequency weight of data elements. Elements that appear earlier or more frequently in the dataset are assigned shorter codewords, while less frequent elements receive longer codewords. This parameter change strategy optimizes the overall compression ratio by adapting codeword lengths to the actual distribution patterns in the data, improving compression performance without requiring fundamentally complex algorithmic changes.
2Productivity
If static codeword assignment is used, then the encoding process is fast and simple, but compression efficiency is limited because it cannot exploit sequential patterns in the data
Solution Approach 1:
The patent applies preliminary action by performing a first pass through the dataset to calculate initial frequency weights for all data elements before actual compression begins. This preliminary analysis allows the algorithm to establish an initial adaptive coding scheme that exploits sequential patterns, improving compression efficiency from the start of the encoding process while minimizing the time penalty of adaptation.
Solution Approach 2:
The patent maintains continuity of useful action by continuously updating the frequency weights and codeword assignments throughout the encoding process. Rather than performing discrete re-encoding passes, the algorithm continuously adapts the coding scheme as it processes each data element, ensuring that compression efficiency improves progressively without significant interruptions or additional time overhead.
3Productivity
If dynamic adjustment of codewords is implemented, then compression performance improves by adapting to data distribution, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by adjusting codeword lengths locally based on the specific frequency weight of each data element rather than applying a uniform coding scheme. Each data element receives a codeword length optimized for its local frequency characteristics, allowing the compression ratio to improve based on the actual local distribution patterns in the data while keeping the overall computational complexity manageable through element-by-element processing.
Data Source
AI summary
There is provided a computer-implemented method of compressing a baseline dataset, comprising: creating a weight function that calculates a weight for each instance of each unique data elements in the baseline dataset, as a function of sequential locations of each of the instances of each respective unique data element within the baseline dataset, creating an output dataset storing a codeword for each one of the unique data elements, wherein codewords are according to a compression rule defining data elements associated with a relatively higher weight as being associated with codewords that are relatively shorter, dynamically creating the compressed dataset by sequentially iterating, for each current sequential location of the baseline dataset: determining an encoded data element mapped to the respective data element of the current sequential location according to the weight function, and adjusting the codewords of the output dataset according to the current weights to maintain the compression rule.