Multimedia File Compression Based on Usage and Quality Aging
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Solution Overview
Problem
Current data storage methods are inefficient in compressing multimedia files, especially those already compressed or encrypted, leading to increased resource costs and user-intensive processes due to limitations in lossless compression and prior restraints on file sizes/types.
Innovation Solution
A file compression system that uses use-based metrics such as file popularity, recent usage, and frequency to select files for lossy compression, optimizing storage by reducing file size while maintaining usefulness, and including metadata to prevent unnecessary re-compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lossless compression is used to preserve data fidelity, then data quality is maintained, but compression effectiveness is poor for already-compressed multimedia files
Solution Approach 1:
The system changes the compression parameter from lossless to lossy compression for multimedia files, accepting some quality degradation to achieve significant file size reduction. This is implemented through quality-based compression that adjusts compression levels according to file characteristics and storage requirements.
Solution Approach 2:
The system applies different compression strategies to different files based on their characteristics. Frequently accessed files maintain higher quality while less frequently accessed files undergo more aggressive compression, creating localized quality optimization throughout the storage system.
2Device complexity
If a priori restraints on file sizes and types are imposed, then storage organization is simplified, but user efficiency is reduced due to manual preprocessing requirements
Solution Approach 1:
The system performs automatic compression and organization of files without requiring user intervention. The compression system autonomously identifies candidate files, applies appropriate compression algorithms, and manages storage optimization, eliminating the need for users to manually preprocess files before upload.
Solution Approach 2:
The system performs compression actions in advance or automatically in the background, so that when users need to store or access files, the compression work has already been completed. This eliminates the need for users to wait for or participate in the compression process.
3Productivity
If repeated compression is applied to maximize storage efficiency, then file sizes are reduced, but file quality deteriorates progressively
Solution Approach 1:
The system applies compression selectively rather than repeatedly to all files. By identifying candidate files that have not yet been compressed or compressed minimally, the system avoids excessive compression that would cause quality deterioration, while still achieving overall storage efficiency improvements.
Solution Approach 2:
The system uses metadata to track compression history and quality levels of files. This feedback mechanism prevents repeated compression of already-compressed files and allows the system to adjust compression strategies to maintain acceptable quality thresholds while improving storage efficiency.
4Productivity
If lossy compression is used to achieve higher compression ratios, then file sizes are reduced significantly, but data fidelity is lost
Solution Approach 1:
The system applies different quality levels of compression to different files based on their access patterns and importance. Frequently accessed files receive lighter compression to preserve quality, while less frequently accessed files undergo more aggressive compression, optimizing the overall balance between fidelity and compression ratio.
Data Source
AI summary
A method and system for optimizing data storage in a storage device. A trigger corresponding to predefined events is received, initiating the optimization of file storage in a storage device. The files are sorted into a list of candidate files according to metadata corresponding to each of the files. A subset of the sorted list of files is selected for compression, based on the position of the candidate files in the sorted list of candidate files. The files comprising the subset of sorted files is compressed by reducing the files in size. The metadata corresponding to the compressed files is updated to reflect the event of the compression.