Context-Based Data Stream Compression Across Non-Adjacent Redundancy
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Solution Overview
Problem
Existing data compression methods only eliminate redundancy between adjacent data, resulting in low compression flexibility and inefficiency for non-adjacent data.
Innovation Solution
Classify data into types based on context information, using predictors and hash tables to identify same and similar data, and apply specific compression algorithms to each type, enhancing redundancy elimination across both adjacent and non-adjacent data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data compression is performed based on adjacent data relationships only, then compression can be implemented with simple methods, but compression flexibility remains low and redundancy between non-adjacent data cannot be eliminated
Solution Approach 1:
The patent segments the data stream into multiple types based on context information, including adjacent data relationships and non-adjacent data relationships. By dividing the compression task into different segments (adjacent type, non-adjacent type, random type), the system can apply specific compression strategies to each segment, thereby improving compression flexibility without overwhelming complexity
Solution Approach 2:
The patent extends the compression approach from a single dimension (adjacent data only) to multiple dimensions by incorporating context information from both adjacent and non-adjacent data positions. This dimensional expansion allows the system to identify and compress redundancy across different data relationships, enhancing flexibility while managing complexity through structured classification
2Productivity
If only adjacent data redundancy is eliminated, then compression implementation is simple, but compression rate is limited due to inability to handle non-adjacent data redundancy
Solution Approach 1:
The patent performs preliminary classification of data into different types (adjacent, non-adjacent, random) before applying compression algorithms. This preliminary action based on context information enables the system to prepare data for optimized compression, improving compression rate by targeting specific redundancy patterns while keeping classification complexity manageable through predefined categories
Solution Approach 2:
The patent applies different compression qualities and strategies to different data types locally. Adjacent data relationships receive one compression treatment, non-adjacent relationships receive another, and random data receive a third. This localized approach improves overall compression rate by optimizing for each data characteristic while avoiding the need to complexify the entire system
3Loss of information
If data is classified into multiple types based on context information, then redundancy between adjacent and non-adjacent data can be eliminated, but data processing complexity increases
Solution Approach 1:
The patent segments data into distinct types (first type for adjacent relationships, second type for non-adjacent relationships, third type for random data) based on context information. This segmentation enables targeted redundancy elimination for each type, improving information compression efficiency while managing system complexity through clear categorization and dedicated processing paths for each segment
Data Source
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AI summary
Embodiments of this application disclose a data compression method and apparatus, a computer device, and a storage medium, and belong to the field of data storage technologies. The method includes: obtaining a to-be-compressed data stream, where the data stream includes a plurality of pieces of data; determining a plurality of types of data from the plurality of pieces of data based on context information of each of the plurality of pieces of data, where each of the plurality of types of data includes at least one of the plurality of pieces of data, and the context information of each piece of data indicates data before and/or after corresponding data in the data stream; and compressing at least one of the plurality of types of data. In embodiments of this application, the data in the data stream may be classified, so that each type of classified data is subsequently compressed. This can eliminate redundancy between pieces of adjacent data and redundancy between pieces of non-adjacent data, thereby improving compression flexibility.