Adaptive Sourceblock Length Prediction for Data Compaction
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Solution Overview
Problem
The rapid growth of data storage demand has outpaced the capacity to store it, leading to storage and transmission bottlenecks, especially with the rise of multimedia data and the need for secure data transmission in quantum computing environments.
Innovation Solution
A system and method using a neural network to predict optimal block sizes for data encoding, leveraging data preprocessing and continuous monitoring to adapt to evolving data patterns, enabling real-time adaptable data compaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is divided into fixed-size blocks for encoding, then the encoding process is simple and consistent, but the compression efficiency is suboptimal when data patterns vary
Solution Approach 1:
The patent applies dynamics by transitioning from fixed-size blocks to variable-length sourceblocks. The encoder dynamically adjusts sourceblock length based on the data being encoded, using techniques like context-adaptive binary arithmetic coding (CABAC) where block size varies according to statistical properties of the data. This resolves the contradiction by making the encoding process adaptive rather than static, improving compression efficiency while maintaining manageable complexity through algorithmic adaptability.
Solution Approach 2:
The patent changes the parameter of block size from a fixed constant to a variable parameter that adapts to data characteristics. By modifying the sourceblock length parameter based on data entropy, symbol frequency, and context information, the system achieves better compression ratios. This parameter change allows the encoding to respond to varying data patterns without requiring fundamentally different encoding approaches.
2Quantity of substance
If traditional compression methods are used, then implementation is straightforward, but storage capacity only doubles and cannot keep pace with exponential data growth
Solution Approach 1:
The patent replaces traditional mechanical compression approaches (fixed algorithms like Huffman or LZW) with a learning-based system that uses neural networks or machine learning models to predict optimal sourceblock lengths and encoding strategies. This substitution enables the system to adapt to diverse data types and patterns, achieving compression ratios that scale with data complexity rather than being limited by fixed algorithmic constraints.
Solution Approach 2:
The patent applies preliminary action by pre-training compression models on large datasets to learn optimal encoding strategies before actual compression. The system performs offline learning to establish statistical models of data patterns, which are then applied during runtime compression. This preliminary preparation enables the system to handle new data types efficiently without requiring real-time algorithm development, scaling compression capability with available training data.
3Quantity of substance
If lossy compression is used to increase storage capacity, then more data can be stored, but data quality degrades
Solution Approach 1:
The patent applies dynamics by making compression aggressiveness adaptive rather than fixed. The system dynamically adjusts between lossless and lossy modes based on data type, application requirements, and available storage capacity. For example, text data may be compressed losslessly while multimedia data uses controlled lossy compression. This dynamic adjustment maintains data quality where critical while maximizing storage capacity where tolerable.
4Productivity
If larger block sizes are used for encoding, then fewer blocks need to be processed, but the ability to adapt to local data variations decreases
Solution Approach 1:
The patent merges the advantages of different block sizes by implementing a hierarchical or multi-scale encoding approach. Large blocks are used for regions with homogeneous data characteristics to improve processing speed, while smaller blocks are used in regions with high variability to maintain adaptability. The system combines these approaches within a unified encoding framework, allowing simultaneous optimization of both processing efficiency and local adaptation.
Data Source
AI summary
A system and method for data compaction optimization which leverages a neural network to predict optimal block sizes for data encoding, enhancing efficiency and adaptability in various applications. It begins with data preprocessing, extracting features, and creating labeled datasets for training. The neural network architecture is carefully designed, allowing it to learn complex relationships between data characteristics and optimal block sizes. During training, the network is fine-tuned and optimized using appropriate loss functions and regularization techniques. Once deployed, it continuously monitors incoming data streams for shifts in data patterns and adapts predictions accordingly. By predicting multiple block sizes, the system accommodates diverse compression needs. This versatile system offers real-time adaptability, ensuring optimal encoding performance as data patterns evolve over time.