Binary Compression Using Dual Data Structures for Lower Latency
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Solution Overview
Problem
Existing data compression methods are inefficient in optimizing compression gain and maximizing performance, particularly in hardware implementations, especially for binary data.
Innovation Solution
The creation of two data structures, a first data structure with zero occurrences of a specific member and a second data structure with a larger number of occurrences, using well-defined rules to 'hop' between preselected pairs of root identifiers, optimizing compression by minimizing file size and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data compression methods are used, then implementation is simpler, but compression gain is not optimized and performance is reduced
Solution Approach 1:
The patent divides the compression system into two distinct data structures: a first data structure with zero occurrences of a specific member, and a second data structure with a larger number of occurrences. This segmentation allows each structure to be optimized for specific compression scenarios, maximizing overall compression gain while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent changes the occurrence parameter of specific members between the two data structures. By creating a first structure with zero occurrences and a second structure with larger occurrences, the system dynamically adjusts parameter distribution to optimize compression efficiency for different data patterns, thereby improving productivity without proportionally increasing device complexity.
2Quantity of substance
If compression is optimized to minimize file size, then storage efficiency improves, but processing time and latency increase
Solution Approach 1:
The patent performs preliminary organization of data into two optimized data structures before the actual compression process. By pre-establishing the first data structure with zero occurrences and the second with larger occurrences, the system prepares the data in advance to enable faster compression processing, thereby reducing latency while achieving minimized file size.
Solution Approach 2:
The patent extracts specific members with different occurrence characteristics into separate data structures. By taking out members with zero occurrences into the first structure and members with larger occurrences into the second structure, the system isolates processing requirements, allowing efficient handling of each group separately and reducing overall processing time while maintaining optimal compression ratios.
Data Source
AI summary
Lossless binary data compression of any arbitrary input file is achieved by describing the file using a finite universal set of binary constructs, creating a file content of occurring binary constructs, organizing the content in sequential groups and slices, with one slice to include several groups and one group to include several binary constructs, forming two or more data structures for each slice with each data structure having specific properties with respect to a chosen binary construct, processing the formed data structures together such that compression gain is achieved, with the process of obtaining compression gain being uniquely reversible, and with when the file compression is complete, the compressed output becomes the input file for another compression cycle, with the cycle repeating until a compression criteria is met.