Grouped Codebook Updating for Entropy Encoding Mismatch
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Solution Overview
Problem
Current data storage technologies face challenges in efficiently handling the exponential growth of data demand due to limited storage capacity and bandwidth constraints, with existing entropy encoding methods failing to effectively compact previously unseen data.
Innovation Solution
A system and method for codebook management that uses mismatch probability estimation to improve entropy encoding by generating a combined codebook based on similarity scores and updating it when performance metrics indicate inefficiencies, incorporating a mismatch codeword to handle unseen data through secondary encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding methods are used, then data compaction is achieved for seen data, but previously unseen data cannot be efficiently encoded leading to poor compression ratios
Solution Approach 1:
The system performs preliminary actions by generating a mismatch codeword in advance and incorporating it into the codebook during the training phase. This preliminary preparation allows the encoding system to immediately handle previously unseen data without requiring retraining or complex runtime decisions, thus resolving the contradiction between efficient compaction of seen data and adaptability to unseen data.
Solution Approach 2:
The mismatch codeword acts as an intermediary element in the codebook that mediates between the encoding of seen data (using standard codewords) and unseen data (using the mismatch codeword). This intermediary allows the system to maintain high compression ratios for seen data while providing a fallback mechanism for unseen data, thereby resolving the technical contradiction.
2Quantity of substance
If more physical storage capacity is added, then storage demand is temporarily met, but storage demand continues to outstrip manufacturing capacity
Solution Approach 1:
The system changes the fundamental parameter of data representation by transforming raw data into compacted form using entropy encoding with mismatch codewords. This parameter change in data format allows significantly more data to be stored in the same physical space, effectively increasing storage capacity without adding physical hardware and addressing the mismatch between data growth and manufacturing capacity.
3Quantity of substance
If data compression is applied, then storage capacity is doubled, but compression ratios decrease for multi-media data and data degradation occurs
Solution Approach 1:
The mismatch codeword functions as a disposable fallback mechanism that is inexpensive in terms of storage space. When unseen data is encountered, the system uses this simple mismatch codeword rather than attempting complex compression that would degrade data quality. This approach prioritizes data integrity over maximum compression for edge cases, resolving the contradiction between storage efficiency and data quality.
4Quantity of substance
If transmission bandwidth is increased, then data transmission capacity is improved, but bandwidth becomes an even greater bottleneck
Solution Approach 1:
The system changes the parameter of data size by applying entropy encoding with mismatch codewords, transforming large data sets into compact representations. This parameter change in data size directly reduces the bandwidth required for transmission, allowing more data to be transmitted over the same bandwidth capacity and addressing the bottleneck issue without increasing physical bandwidth infrastructure.
Data Source
AI summary
A system and method for codebook management is disclosed. Training datasets are obtained from various data sources. A similarity score is generated for each training dataset with reference to the other training datasets. In response to detecting a similarity score above a predetermined threshold for one or more of the other training datasets, a combined codebook is created based on training datasets that have a similarity score above a predetermined threshold. Based on the similarity score, multiple data sources are combined into a group, and the combined codebook is used for the data sources within the group. A mismatch performance metric can be computed for the combined codebook, and a revised combined codebook can be regenerated in response to the mismatch performance metric being above a predetermined threshold.