Entropy Codebook Compaction With Mismatch Encoding for Unseen Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data storage technologies face challenges in efficiently handling rapidly increasing data demands due to limited storage capacity and bandwidth constraints, especially with the rise of multimedia data and the need for enhanced security as quantum computing approaches, where existing entropy encoding methods fail to effectively compact previously unseen data.
Innovation Solution
A system and method utilizing mismatch probability estimation to improve entropy encoding by calculating the frequency of occurrence of data sourceblocks, generating a codebook with mismatch codewords for unseen data, and employing a secondary encoding process when mismatches occur during encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding methods are used, then data compaction is achieved for known data patterns, but previously unseen data cannot be efficiently encoded leading to poor compression ratios
Solution Approach 1:
The system performs preliminary analysis on training data to build a codebook of expected sourceblocks before actual encoding begins. This preliminary action creates a foundation for efficient encoding of known patterns while preparing for mismatches. The mismatch probability estimate is calculated in advance based on training data characteristics, allowing the system to pre-prepare appropriate codeword lengths and structures for both matching and mismatching scenarios.
Solution Approach 2:
The system dynamically adapts its encoding strategy by switching between two modes: using the pre-built codebook for matching sourceblocks and employing a secondary encoding process for mismatched sourceblocks. The mismatch probability estimate dynamically guides this decision-making, allowing the system to optimize compression for common patterns while maintaining efficiency for rare or unseen patterns. This dynamic adaptation resolves the contradiction between efficiency for known data and versatility for unknown data.
2Productivity
If codebooks are generated based on training data frequency, then common data patterns achieve high compression, but the codebook size and complexity increase to accommodate all possible sourceblocks
Solution Approach 1:
The system applies different quality levels of encoding to different portions of the data based on their characteristics. High-frequency sourceblocks from the training data receive optimized short codewords in the codebook, while low-frequency or unseen sourceblocks are handled by the secondary encoding process. This local differentiation allows the codebook to remain compact while still providing excellent compression for the majority of common patterns, resolving the contradiction between compression efficiency and codebook complexity.
3Adaptability or versatility
If mismatch probability estimation is incorporated, then previously unseen data is handled more efficiently, but the encoding process complexity increases due to secondary encoding requirements
Solution Approach 1:
The mismatch probability estimate serves as an intermediary that bridges the simple codebook lookup process and the more complex secondary encoding process. By calculating this probability based on training data, the system can make informed decisions about when to use each encoding method. This intermediary mechanism adds minimal complexity while significantly improving the handling of unseen data, as it provides a quantitative basis for selecting the appropriate encoding strategy without requiring the system to prepare for all possible unseen scenarios in advance.
Data Source
AI summary
A system and method for compacting data that uses mismatch probability estimation to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Entropy encoding is used to generate codebooks comprising codewords for data sourceblocks based on the frequency of occurrence of each sourceblock. A “mismatch codeword” is inserted into the codebook based on the mismatch probability estimate to represent those cases when a block of data to be encoded does not have a codeword in the codebook. During encoding, if a mismatch occurs, a secondary encoding process is used to encode the mismatched sourceblock.