Deep Learning Compression With Protocol-Adaptive Decoding
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Solution Overview
Problem
Current data storage technologies are struggling to keep pace with the rapidly increasing demand for data storage, as data growth has accelerated exponentially, exceeding the capacity for physical storage, and traditional compression methods are inefficient and adaptable to evolving data types.
Innovation Solution
A system and method for data compression with protocol adaptation using a codebook generator that leverages machine/deep learning algorithms to generate a protocol appendix and codebook, enabling efficient encoding and dynamic protocol adaptation during decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used, then storage capacity is increased, but compression ratio is insufficient and cannot adapt to evolving data types
Solution Approach 1:
The patent implements dynamic protocol adaptation during the decoding process, allowing the system to adjust and transform data into different protocol formats based on the requirements of the target system. This dynamic capability enables the compression system to handle evolving data types and communication protocols without requiring complete system redesign.
Solution Approach 2:
The patent creates a universal compression framework that can handle multiple data types and protocol formats through a single system. The protocol adaptation layer provides multi-functionality by transforming compressed data into various protocol formats, making the system applicable to diverse communication scenarios and data types.
2Reliability
If lossless compression is used, then data integrity is maintained, but space savings are insufficient for multi-media data
Solution Approach 1:
The patent enables dynamic switching between lossless and lossy compression modes based on the specific requirements of the application and the type of data being compressed. This dynamic approach allows the system to optimize the balance between data integrity and compression ratio for different scenarios, including multi-media data where some loss may be acceptable.
3Adaptability or versatility
If protocol transformation is added to the decoding process, then interoperability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a protocol adaptation layer as an intermediary component between the decompression engine and the target system. This mediator handles the complexity of protocol transformation, shielding the core compression algorithms from protocol-specific details while enabling interoperability with various communication protocols and systems.
4Quantity of substance
If deep learning algorithms are used for compression, then compression ratio is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent performs preliminary training of deep learning models offline to learn compression patterns and characteristics of different data types. Once trained, these models can be deployed for rapid compression of new data without requiring extensive processing time during actual compression operations, thus reducing the time penalty associated with using sophisticated algorithms.
Data Source
AI summary
A system and method for data compression with protocol adaptation, that utilizes a codebook generator which leverages one or more machine/deep learning algorithms trained on at least a plurality of protocol policies in order to generate a protocol appendix and codebook, wherein original data is encoded by an encoder according to the codebook and sent to a decoder, but instead of just decoding the data according to the codebook to reconstruct the original data, data manipulation rules such as mapping and transformation are applied at the decoding stage to transform the decoded data into protocol formatted data.