Symbol String Compression for Lower Bandwidth Data Transmission
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Solution Overview
Problem
Current data compression methods are inefficient in reducing bandwidth utilization during data transmission and storage, particularly when dealing with large datasets, as they often fail to effectively utilize repetitive patterns and context-dependent coding.
Innovation Solution
A method involving the identification and coding of symbol strings within data sets, using techniques like run length coding and significance switching to generate a compressed data code that represents the positions of these strings, allowing for efficient storage or transmission without loss of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If current data compression methods are used, then data can be stored or transmitted, but bandwidth utilization is not effectively reduced and compression efficiency is poor
Solution Approach 1:
The patent segments the data into fixed-length symbol strings of length K, where K is a power of 2. This segmentation allows for systematic processing and coding of repetitive patterns, enabling more effective compression by treating groups of symbols as unified units rather than individual elements.
Solution Approach 2:
The patent employs variable-length coding where the number of bits used to code symbol string positions varies based on the distance between occurrences. Specifically, it uses 2 bits for positions within the same block and additional bits for positions spanning multiple blocks, optimizing the representation based on the actual data characteristics.
2Quantity of substance
If data is compressed using traditional methods, then some reduction is achieved, but repetitive patterns and context-dependent coding are not effectively utilized
Solution Approach 1:
The patent performs preliminary segmentation of the data into fixed-length symbol strings before coding. This preliminary organization into blocks of K symbols creates a structured foundation that enables more efficient subsequent coding operations, particularly in identifying and compressing repetitive patterns across the data set.
Solution Approach 2:
The patent introduces block numbers and position codes as intermediary representations. Instead of directly coding raw symbol positions, it uses block-based indexing systems where positions are represented relative to block boundaries, creating an intermediate layer that simplifies the compression of repetitive patterns.
3Ease of operation
If symbol positions are coded with fixed bit allocation, then coding is simple, but compression efficiency is reduced due to unnecessary bits
Solution Approach 1:
The patent implements dynamic bit allocation for position coding based on the distance between symbol occurrences. When symbols appear within the same block, only 2 bits are used; when they span multiple blocks, additional bits are allocated. This dynamic approach adapts the coding scheme to the actual data characteristics, maximizing compression efficiency.
Solution Approach 2:
The patent applies different coding strategies to different regions of the data based on local characteristics. Within-block positions use a simplified 2-bit coding scheme, while between-block positions use extended coding with additional bits. This local differentiation optimizes the balance between coding simplicity and compression efficiency for different parts of the data.
Data Source
AI summary
Techniques for data compression are provided. A data compression technique may include selecting a symbol string, being one or more characters, that occurs within a data set, generating a symbol string code corresponding to respective positions of the symbol string within the data set, successively repeating the above procedure for any additional symbol strings that occur within the data set to produce any additional respective symbol string codes, and generating a compressed data code through combining the respective symbol string codes.