Multi-Type Data Compression Platform With Neural Upsampling Recovery
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Solution Overview
Problem
Existing data compression technologies often inefficiently compress or fail to compress associated metadata, leading to loss of information, especially when using lossy compression methods for visual or auditory data.
Innovation Solution
A unified platform employing a virtual management layer to organize and route input data to corresponding compression subsystems, utilizing multiple compression methods including homomorphic encryption-based techniques, to efficiently compress and decompress various data types while maintaining data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lossy compression techniques are used for visual or auditory data, then bandwidth efficiency is improved, but data quality and information completeness deteriorate
Solution Approach 1:
The system segments data into multiple types (visual/auditory data and associated metadata) and applies different compression techniques to each segment. Lossy compression is applied to visual/auditory data where bandwidth efficiency is prioritized, while lossless compression is applied to metadata where information completeness is critical, thereby resolving the contradiction between bandwidth efficiency and data quality.
Solution Approach 2:
Different compression quality levels are applied to different parts of the data based on their importance. Visual/auditory data receives lossy compression with acceptable quality degradation, while associated metadata receives lossless compression to preserve complete information, allowing the system to optimize bandwidth usage without sacrificing critical information.
2Device complexity
If a single compression technique is applied to all data types, then system complexity is reduced, but compression efficiency and information preservation deteriorate
Solution Approach 1:
The system implements a universal compression platform that can handle multiple data types (visual, auditory, metadata) with different compression requirements through a unified architecture. The platform automatically selects appropriate compression techniques for each data type, providing multi-functionality without requiring separate complex systems for each data type, thus maintaining manageable system complexity while achieving high compression efficiency.
Solution Approach 2:
The compression system dynamically adapts its technique selection based on the input data type and requirements. The system can switch between lossy and lossless compression methods, adjust compression parameters, and route different data types to appropriate processing pipelines, enabling efficient compression across diverse data types without static system configuration.
3Loss of information
If lossless compression is used for all data including metadata, then information completeness is improved, but bandwidth efficiency and compression ratio deteriorate
Solution Approach 1:
The system segments data into categories based on information criticality. Associated metadata is identified and separated from visual/auditory data, then routed to lossless compression processes that preserve complete information. Visual/auditory data is routed to lossy compression processes that achieve higher bandwidth efficiency, optimizing the trade-off between information completeness and bandwidth usage.
Solution Approach 2:
The system changes compression parameters (lossless vs. lossy) based on data type characteristics. For metadata where information completeness is paramount, lossless compression parameters are applied. For visual/auditory data where bandwidth efficiency is more important, lossy compression parameters are applied, allowing optimal bandwidth utilization while preserving critical information.
Data Source
AI summary
A unified platform for multi-type data compression and decompression is disclosed. The platform employs a virtual management layer to receive, organize, and route input data to corresponding compression subsystems. Multiple compression methods, including homomorphic encryption-based techniques, are utilized to compress data sets while maintaining data privacy. A data manager associates and manages related data sets throughout the compression and decompression processes. Compressed data is routed to appropriate decompression subsystems, where it is decompressed and reconstructed using advanced techniques, such as neural upsampling, to recover lost information and enhance data quality. The platform supports various data types and compression methods, enabling efficient and secure compression and decompression of data. By integrating homomorphic encryption and data reconstruction techniques, the platform provides a comprehensive solution for data compression and decompression while preserving data privacy and enhancing data quality.