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On Demand Secure Scalable Video Streaming for Both Human and Machine Applications.
Alaa Zain1, Yibo Fan2, Jinjia Zhou1
1Faculty of Science and Engineering, Department of Applied Informatics, Koganei Campus, Hosei University, Tokyo 102-8160, Japan.
This study introduces adaptive scalable video coding with intelligent encryption, enhancing security and machine analysis. The new method improves efficiency for both human viewers and automated systems.
Area of Science:
- Computer Science
- Information Security
- Signal Processing
Background:
- Scalable video coding is crucial for diverse streaming needs but often lacks privacy and machine analysis support.
- Existing methods prioritize human perception, limiting utility for automated systems and data security.
- Need for video coding that balances human quality, machine analysis, privacy, and compression efficiency.
Purpose of the Study:
- Develop an adaptive scalable video coding framework for dual human and machine use.
- Integrate intelligent encryption for enhanced data privacy and security.
- Improve compression efficiency and machine analysis performance over existing methods.
Main Methods:
- Proposed an adaptive scalable video coding framework.
- Implemented adaptive layer encryption for sensitive content protection.
- Evaluated performance using benchmark datasets and video surveillance scenarios.
Main Results:
- Achieved superior rate distortion efficiency and reconstruction quality.
- Demonstrated improved machine analysis accuracy compared to traditional and learning-based codes.
- Scalable Human Machine Coding (SHMC) reduced bit-per-pixel by up to 60.29% in surveillance scenarios at equivalent quality metrics.
Conclusions:
- The proposed framework effectively integrates scalable video coding with intelligent encryption.
- This approach ensures secure, efficient video transmission for both human and machine consumption.
- SHMC offers significant advantages over HEVC/SHVC extensions in specific applications like video surveillance.
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