Fixed-Length Data Compression Using Bit Reordering Buffers
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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 of data compression that combines fixed-size binary data structures into composite structures, aligns bits, calculates bit probabilities, and reorders them based on a re-ordering model to create intermediate buffers, which are then traversed and encoded using delta bit string buffers or bitwise XOR operations to reduce data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed-size binary data structures are transmitted without compression, then data transmission is simple and fast, but network traffic volume is large which slows down overall gameplay
Solution Approach 1:
The patent segments fixed-size binary data structures into individual bits and reorganizes them by position across multiple structures. This segmentation allows the system to identify and compress only the variable portions of the data while maintaining the fixed-size requirement for game logic operations, thereby reducing network traffic without compromising transmission efficiency
Solution Approach 2:
The patent changes the parameter representation by calculating bit probabilities and reordering bits based on their variability. Less variable bits (lower probability of change) are compressed more aggressively, while more variable bits maintain higher fidelity. This parameter-based approach reduces overall network traffic volume while preserving the essential information needed for real-time gameplay
2Productivity
If data compression is applied to reduce network traffic, then data transmission efficiency improves, but processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating bit probabilities and creating reordering models before actual data compression. These pre-computed models are reused for compressing multiple data structures, reducing the per-operation complexity while maintaining high compression efficiency. The initial setup work pays off in reduced processing complexity for subsequent compressions
Solution Approach 2:
The patent creates simplified copies or representations of the data in a compressed format that preserves essential information. By working with compressed bit representations rather than full fixed-size structures, the system reduces processing complexity during transmission while maintaining the ability to reconstruct the original data when needed for game logic operations
3Quantity of substance
If existing compression methods are used, then some data reduction is achieved, but fixed-length binary data structures cannot be effectively compressed
Solution Approach 1:
The patent applies local quality by treating different bit positions within fixed-size binary data structures differently based on their variability. Each bit position is analyzed independently and assigned a compression strategy based on its probability of change. This localized approach allows effective compression of specific bits while maintaining the overall fixed-length structure requirement, making the compression method adaptable to fixed-size data constraints
Solution Approach 2:
The patent transitions from viewing data as individual fixed-size structures to viewing it as a multi-dimensional array where rows represent structures and columns represent bit positions. This dimensional transformation enables the system to apply compression across the column dimension (bit positions) while preserving the row dimension (individual structures) integrity, effectively compressing fixed-length data without losing structural compatibility
Data Source
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.