基于ETRN具有任意压缩率的彩色加密图像有损压缩

当前大多数的先加密后压缩ETC（encryption-then-compression）方法只能够获得有限固定的压缩率，而无法获取到实际需求的任意压缩率。针对此问题提出一种具有任意压缩率的加密彩色图像有损压缩算法，该算法采用均匀下采样和随机下采样有机结合的方式对加密图像进行压缩，以获得加密图像的任意压缩率。接收方接收到加密图像的压缩序列后通过解压解密获得解密图像，随后把从解密图像有损重构原始图像的过程表征为一个结合下采样压缩方式约束的最优化问题，并设计一种基于卷积神经网络的有损ETC系统图像重构模型ETRN（ETC-oriented reconstruction network）来求解该优化问题。ETRN模型包含浅层特征提取层SFE（shallow feature extraction）、残差堆叠模块RIR（residual in residual）、残差信息补充模块RCS（residual content supplementation）、下采样约束模块DC（down-sampling constraint）。实验仿真结果表明，提出的加密彩色图像有损压缩算法能够获得优秀的加密压缩和重构性能，充分体现了该方法的可行性和有效性。

ETRN-based lossy encryption-then-compression scheme on color images with arbitrary compression ratio

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.