A Customized Deep Network Based Encryption-Then-Lossy-Compression Scheme of Color Images Achieving Arbitrary Compression Ratios

As encryption masks the content of the original image and thus statistical characteristics of the original image cannot be used to compress the encrypted version, compressing an encrypted image efficiently remains a significant challenge today. In this study, a novel encryption-then-lossy-compression (ETLC) scheme was developed using nonuniform downsampling and a customized deep network. Specifically, the nonuniform downsampling method integrates both uniform and random sampling to achieve an arbitrary compression ratio for an encrypted image. Lossy reconstruction from the decrypted and decompressed image is described as a constrained optimization problem, and an ETLC-oriented customized deep neural network (ETCNN) is elaborately designed to solve this problem. ETCNN contains three parts: channel-wise non-local attention including residual group and non-local sparse attention, a residual content supplementation (RCS), and a downsampling constraint (DC), where RCS and DC are customized modules exploiting specific features of the downsampling-based ETLC system. Extensive experimental simulations show that the proposed scheme outperforms the state-of-the-art ETLC methods remarkably, indicating the feasibility and effectiveness of the proposed scheme exploiting the nonuniform downsampling and ETCNN-based reconstruction. Code is available at
https://github.com/hujuanzp/ETCNN
.