Codebook Statistical Estimates for Unseen Entropy Encoding Mismatches
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Solution Overview
Problem
Existing entropy encoding methods fail to efficiently handle previously-unseen data, leading to inefficient data compaction and transmission, especially with the exponential growth of data storage and transmission demands outpacing physical capacity and bandwidth.
Innovation Solution
A system and method using mismatch probability estimation to generate codebooks with codewords based on data frequency, incorporating a mismatch codeword to handle unseen data, and employing a secondary encoding process when mismatches occur.
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 encoding efficiency deteriorates when previously-unseen data is encountered
Solution Approach 1:
The system performs preliminary actions by pre-computing mismatch probabilities during a training phase using training data. These probability estimates are stored and reused during actual encoding operations, eliminating the need to compute mismatch probabilities on-the-fly when encoding unseen data. This preliminary preparation enables efficient handling of previously-unseen data without sacrificing encoding performance.
Solution Approach 2:
The system implements a mismatch codeword that allows the encoder to quickly skip the complex mismatch detection and handling process. When a mismatch is detected, the system immediately uses the pre-computed mismatch codeword instead of performing lengthy computations to handle the unseen data, thereby maintaining high encoding efficiency even when encountering previously-unseen data patterns.
2Manufacturing precision
If codebook size is increased to cover more data patterns, then encoding accuracy for diverse data improves, but storage and processing requirements increase
Solution Approach 1:
The system changes the parameter approach by not expanding the codebook size but instead introducing a probabilistic parameter (mismatch probability) that captures the likelihood of encountering unseen data. This allows the system to maintain a compact codebook while accurately representing diverse data patterns through the statistical mismatch probability derived from training data.
Solution Approach 2:
The mismatch probability serves as an intermediary between the limited codebook and the potentially infinite space of unseen data patterns. Rather than directly encoding every possible pattern (which would require an enormous codebook), the system uses the mismatch probability as a mediator to efficiently represent and encode previously-unseen data with a compact codebook structure.
Data Source
AI summary
A system and method for data compaction with codebook statistical estimates 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.