Suffix Trie Bit Prediction for Adaptive Data Compression
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Solution Overview
Problem
Existing bit prediction algorithms are complex, nonflexible, and require significant time and effort to achieve high performance, making them difficult to adapt for different types of data.
Innovation Solution
A statistical model represented by a Suffix-Trie structure is used for bit prediction, where binary event probabilities are determined and entropy coded, allowing for efficient adjustment and updating of probabilities to predict the next bit in a bit stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing bit prediction algorithms are used, then performance can be improved through fine-tuning, but the complexity and time required for optimization increases significantly
Solution Approach 1:
The patent segments the bit prediction problem into multiple contexts represented by a trie data structure, where each node represents a specific context state. This segmentation allows the system to handle different data patterns separately, improving prediction accuracy without requiring complex global optimization for the entire data stream.
Solution Approach 2:
The patent implements dynamic context adaptation by maintaining a trie structure that can be dynamically updated as new data is processed. The context model adapts to different data types automatically through traversal and updates, eliminating the need for manual fine-tuning while maintaining high performance across various data types.
2Reliability
If existing bit prediction algorithms are optimized for high performance, then prediction accuracy improves, but flexibility and adaptability to different data types decreases
Solution Approach 1:
The patent creates a universal bit prediction system based on a trie structure that can handle multiple data types and contexts. The same core algorithm and data structure adapt to different data types automatically, providing both high accuracy and versatility without requiring separate optimization for each data type.
Solution Approach 2:
The dynamic trie structure allows the system to adapt to different data types by traversing appropriate paths and updating contexts based on the input data characteristics. This dynamic adaptation maintains high prediction accuracy across diverse data types without sacrificing flexibility.
3Reliability
If extensive fine-tuning is performed to achieve top performance, then bit prediction accuracy improves, but the time and effort required increases significantly
Solution Approach 1:
The patent implements a self-adapting system where the trie structure automatically learns and adjusts to the input data characteristics during normal operation. The system performs its own optimization by dynamically updating context probabilities and traversing appropriate paths, eliminating the need for external fine-tuning while maintaining high accuracy.
Solution Approach 2:
The patent pre-structures the prediction model using a trie data structure that anticipates different data patterns and contexts. This preliminary structuring allows the system to quickly adapt to new data types without requiring time-consuming optimization, as the framework is already in place to handle various contexts efficiently.
Data Source
AI summary
Methods and apparatus for bit prediction and data compression are provided. In the bit prediction method and apparatus, a specific Suffix Trie is used to maintain a statistical model to predict the next bit in a bit-stream. The statistic model provides the probability for each next or following bit in the bit stream, where an entropy encoder/decoder further encodes/decodes the predicted bit. The algorithm simplicity and its high performance relies on the combination of the specific method of Suffix Trie construction and growing, and the specific way to compute, update and propagate the probability across Suffix Trie nodes. The specific and particular method to grow the Suffix Trie in conjunction with the specific method to compute, update, and propagate the probability across Suffix Trie nodes are key aspects and subject matter of the present invention.