Entropy Codebook Mismatch Handling for Unseen Data Compaction
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Solution Overview
Problem
Current data storage technologies face challenges in efficiently handling increasing data demands due to limited storage capacity and bandwidth limitations, especially with the rise of multimedia data and the need for enhanced security, as existing entropy encoding methods fail to account for previously unseen data effectively.
Innovation Solution
A system and method using mismatch probability estimation to improve entropy encoding by calculating the frequency of occurrence of data sourceblocks and incorporating a mismatch codeword into the codebook, allowing for efficient handling of unseen data through a secondary encoding process during encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing entropy encoding methods are used, then data compaction is achieved, but previously unseen data cannot be handled effectively leading to encoding failures or inefficiency
Solution Approach 1:
The system performs preliminary actions by training on a training data set before actual encoding. During training, it analyzes source blocks and builds a codebook with codewords for frequently occurring data patterns. This preliminary codebook construction enables the system to efficiently encode common patterns while having a prepared mechanism (mismatch codeword) to handle unseen data during the actual encoding phase.
Solution Approach 2:
The mismatch codeword acts as an intermediary element in the codebook that mediates between the entropy encoding process and previously unseen data. When the encoder encounters a source block not present in the training data, it uses the mismatch codeword as a placeholder, which then triggers a secondary encoding process. This intermediary mechanism allows the system to gracefully handle unseen data without failing the encoding process.
2Quantity of substance
If physical storage capacity is increased to meet data demand, then storage capacity is improved, but the cost and feasibility are limited by manufacturing capacity
Solution Approach 1:
The system changes the parameter of data representation by transforming raw data into a compact encoded form using entropy encoding with a codebook. Instead of storing data in its original form, the system stores frequency information from training data and uses this to generate compact representations. This parameter change from storing actual data to storing encoding mappings dramatically increases effective storage capacity without requiring proportional increases in physical storage manufacturing.
3Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency is improved, but compression ratios are limited and data degradation occurs with lossy compression
Solution Approach 1:
The system changes the approach from traditional compression algorithms to entropy-based encoding with mismatch handling. By using a codebook built from training data and incorporating a mismatch codeword mechanism, the system achieves better compression ratios while maintaining lossless reconstruction capability. The mismatch codeword ensures that even previously unseen data can be encoded and perfectly reconstructed, avoiding the data degradation inherent in lossy compression methods.
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.