Most of the ETC methods can only obtain several limited fixed compression ratios. However, arbitrary encryption compression ratios instead of limited fixed compression ratios are more suitable for practical requirements. To this end, this paper proposed a lossy compression algorithm for encrypted color images with arbitrary compression ratios. It combined the uniform and random downsampling to compress the encrypted images, obtaining arbitrary compression ratios of an encrypted image. The receiver received the compressed sequence of the encrypted image and obtained the decrypted image by decompression and decryption. The proposed scheme then characterized the lossy reconstruction of the original image from the decrypted image as an optimization problem with the downsampling compression-based constraint. This scheme designed a convolutional neural network-based image reconstruction model for lossy ETC to resolve this problem, which was denoted the ETRN. ETRN consisted of SFE, RIR, RCS, and DC. The experimental simulation results show that the proposed encrypted color image lossy compression algorithm can obtain excellent compression and reconstruction performance, which fully demonstrates the feasibility and effectiveness of this method.

[2023-03-03] Accepted Paper /doc/2022.12.0803/v1

[2023-08-05] Printed Article /doc/2022.12.0803/v2