Fixed-Length Data Compression Using Bit Probability Reordering
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Solution Overview
Problem
Existing data compression methods for real-time multi-player games are inefficient in reducing network traffic, particularly when handling fixed-length binary data structures, which can hinder player experience due to slower data transmission speeds.
Innovation Solution
A method involving the concatenation and alignment of fixed-size binary data strings, calculation of bit probabilities, re-ordering based on variance, and delta coding with XOR operations to compress and encode data, allowing for efficient compression and decompression of fixed-length data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data compression methods are used for fixed-length binary data structures, then data transmission occurs, but network traffic reduction is insufficient and data transmission speed is slow
Solution Approach 1:
The method segments fixed-length binary data structures into individual bits and reorders them based on probability analysis. By dividing the data into bit-level components and rearranging them according to variance calculations, the compression algorithm achieves more efficient encoding that reduces overall network traffic volume while maintaining transmission speed.
Solution Approach 2:
The invention changes the parameter arrangement within fixed-length binary data by calculating bit probabilities and reordering bits based on variance. This parameter reorganization allows subsequent compression steps to achieve better compression ratios, effectively reducing network traffic volume without sacrificing transmission efficiency.
2Quantity of substance
If data is compressed by re-ordering bits based on probability, then compression efficiency improves, but calculation complexity increases
Solution Approach 1:
The method performs preliminary probability calculations and bit reordering before the actual compression process. By pre-calculating bit probabilities and arranging bits in optimal order beforehand, the subsequent compression operations become more efficient, achieving better compression ratios without proportionally increasing overall computational complexity.
3Productivity
If fixed-length binary data structures are transmitted without optimization, then data integrity is maintained, but data transmission efficiency is low
Solution Approach 1:
The invention transforms the data representation by moving from byte-level or field-level organization to bit-level reordering based on probability dimensions. This dimensional change allows the compression algorithm to exploit statistical patterns across bit positions, achieving more efficient compression that reduces transmission volume while maintaining data integrity through reversible transformation.
Data Source
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AI summary
The disclosure is directed at a method of data compression. The method includes creating a set of single composite data structures and then calculating a set of bit probabilities based on the set of single data structures. The bit probabilities are then used to create a set of intermediate buffers which are then sorted and traversed for data compression.