Cognitive Data Compression via Encoder-Decoder Feedback
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Solution Overview
Problem
Conventional lossless data compression methods do not effectively utilize contextual redundancy, leading to inefficient data representation and increased decoding effort.
Innovation Solution
A cognitive data compression method employing a feedback loop between an encoder and a decoder, where the systems learn from each other to optimize compression without pre-annotated data, using contextual information to reduce data size while maintaining efficient decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional lossless data compression is used based on Shannon Theorem, then data size is reduced by eliminating symbol redundancy, but contextual redundancy is not utilized effectively leading to increased decoding effort
Solution Approach 1:
The patent implements a feedback mechanism where the encoder and decoder continuously exchange information about the dictionary state and compression parameters. The encoder learns from the decoder's performance and adjusts the compression strategy, while the decoder uses this feedback to optimize its reconstruction process. This closed-loop system resolves the contradiction by dynamically balancing compression ratio and decoding complexity based on actual performance metrics.
Solution Approach 2:
The patent applies preliminary action by pre-training the encoder and decoder systems with representative data before actual compression operations. This pre-training phase establishes initial dictionaries and compression strategies that are optimized for the specific data type, reducing the computational effort required during actual decoding operations while maintaining effective compression ratios.
2Ease of operation
If conventional compression algorithms use fixed dictionaries, then decoding is simpler, but adaptability to different data contexts is reduced
Solution Approach 1:
The patent implements dynamic dictionaries that evolve and adapt during the compression process. Rather than using fixed dictionaries, the system continuously updates its vocabulary based on the specific data being compressed, allowing optimal adaptation to different contexts. This dynamic approach maintains decoding simplicity through structured updates while significantly improving adaptability to various data types and patterns.
Solution Approach 2:
The system changes key parameters of the compression algorithm, specifically the dictionary structure and composition, based on the data context. By dynamically adjusting dictionary size, update frequency, and selection criteria, the system achieves both adaptability to different contexts and controlled complexity in the decoding process.
Data Source
AI summary
A compression method, system, and computer program product include creating compressed data via a first system from input data, sending information to a second system detailing a compression strategy for the compressed data, and learning, via the second system, from the information how to recreate the input to the first system using the compressed data.