Robust image compression‐encryption via scrambled block bernoulli sampling with diffusion noise. Issue 5 (30th December 2022)

• Record Type: Journal Article
• Authors: Chen, Zan; Ma, Chaocheng; Wang, Tao; Feng, Yuanjing; Hou, Xingsong; Qian, Xueming
• Is Part Of: IET image processing. Volume 17:Issue 5(2023)
• Page Start: 1478
• Page End: 1492
• Publication Date: 2022-12-30
• Subjects: compressed sensing -- image coding; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12731
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: This paper proposed an image compression‐encryption scheme based on compressive sensing theory, which achieves high security, strong robustness, and high rate‐distortion performance. First, the denoising preprocessing strategy is applied at the encoder side, which can enhance the rate‐distortion performance without sacrificing security and robustness. Second, the preprocessed image is randomly down‐sampled using scrambled block Bernoulli sampling with diffusion noise (SBBS‐DN), which is generated by combining a hyper‐chaotic system and SHA256 hash of the plain image. Third, a deep‐learned plug‐and‐play is embedded prior for plain image reconstruction at the decoder side. Simulation results show that the proposed scheme has desirable security performance (being resistant to different attacks), high R‐D performance (PSNR gains over 1.3 dB than JPEG at 0.50 bpp compression ratio), and high error resilience (reconstructed 29.92 dB at 0.50 bpp compression ratio even with 50% bit loss). Abstract : This paper proposes an image compression‐encryption scheme based on compressive sensing theory, which achieves high security, strong robustness, and high rate‐distortion performance.