Robust image compression-encryption via scrambled block bernoulli sampling with diffusion noise

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).