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Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps.

Xinsheng Li1, Taiyong Li2, Jiang Wu2

  • 1College of Computer Science, Sichuan University, China.

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|November 19, 2019
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Summary
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This study introduces a new method for joint image compression and encryption using a quantum chaotic system, sparse Bayesian learning (SBL), and a 3D Arnold cat map. The QSBLA approach offers a promising solution for efficient and secure image processing.

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Area of Science:

  • Digital Image Processing
  • Information Security
  • Quantum Computing Applications

Background:

  • Image compression and encryption are critical for efficient and secure data handling.
  • Combining compression and encryption is an active research area.
  • Existing methods face challenges in balancing efficiency and security.

Purpose of the Study:

  • To propose a novel joint image compression and encryption approach.
  • To integrate quantum chaotic systems, sparse Bayesian learning (SBL), and bit-level 3D Arnold cat maps.
  • To enhance image processing security and efficiency.

Main Methods:

  • Utilized a quantum chaotic system for chaotic sequence generation.
  • Employed sparse Bayesian learning (SBL) for image compression (compressive sensing).
  • Implemented diffusion, bit-level cube transformation, and 3D Arnold cat map permutation for encryption.

Main Results:

  • The proposed QSBLA method was evaluated on 8 public images.
  • Experimental results show QSBLA is superior or comparable to state-of-the-art methods.
  • Performance was assessed using multiple measurement indices.

Conclusions:

  • The QSBLA approach effectively achieves joint image compression and encryption.
  • The method demonstrates significant potential for practical applications.
  • Further research can explore optimizations and broader applications.