ML Codebook Compression With Protocol-Adaptive Decoding
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity of physical storage solutions, and the limitations of data compression methods, particularly with multimedia data, lead to bottlenecks in data transmission and storage efficiency.
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 data encoding and decoding while applying data manipulation rules to transform data into protocol-formatted data, enhancing interoperability and storage efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency improves, but data transmission bandwidth requirements worsen due to compression overhead and multimedia data limitations
Solution Approach 1:
The codebook is generated and prepared in advance before actual data encoding occurs. The machine learning model is trained beforehand to understand protocol policies and data patterns, creating a ready-to-use codebook that enables rapid encoding without real-time computation overhead, thus improving transmission efficiency while maintaining compression ratios
Solution Approach 2:
Traditional compression algorithms are replaced with a machine learning-based encoding system. The ML model learns optimal encoding strategies from training data and protocol policies, substituting mechanical compression rules with adaptive intelligent decision-making, achieving better compression ratios without bandwidth overhead
2Adaptability or versatility
If protocol adaptation is added during decoding to improve interoperability, then system compatibility improves, but device complexity worsens due to additional processing layers
Solution Approach 1:
The protocol adaptation functionality is merged into the codebook generation process rather than being a separate decoding step. The machine learning model learns protocol policies during training and integrates them into the codebook structure, so that a single decoding operation using the pre-trained codebook achieves both decompression and protocol adaptation, reducing overall system complexity
Solution Approach 2:
The codebook generator performs self-adaptation to different protocols through machine learning. Instead of requiring manual configuration or complex adaptation logic in the decoder, the system automatically learns protocol characteristics from training data and configures the codebook accordingly, enabling the decoder to work with multiple protocols using the same simple structure
3Productivity
If machine learning algorithms are used to generate codebooks for compression, then compression efficiency improves, but computational requirements worsen during the training phase
Solution Approach 1:
The computational workload is segmented into two distinct phases: an offline training phase where the machine learning model learns from data and protocol policies, and an online encoding phase where the pre-trained model rapidly compresses data using the generated codebook. This segmentation allows heavy computation to occur once during training, while operational compression requires minimal computational resources
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.