基于统计模型进行率失真优化的加密图像压缩算法

A compression scheme on encrypted grey images exploiting the statistic model and rate distorting optimization

如何对加密数据进行有效压缩和高质量重构是云计算环境中颇具挑战性的一个研究问题。其挑战性主要来自于云用户的加密操作掩盖了载体数据的统计特性,从而使得云端的压缩很难像常规压缩那样充分利用载体的统计特性。在小波系数统计模型吻合度评估的基础上,提出了一种基于统计模型进行率失真优化的加密图像压缩算法。将灰度图像经提升小波分解后的低频子带和小波子带分别进行流密码加密和置乱加密,然后再分别进行无损和有损压缩,最后进行相应的逆操作而重构原图像。鉴于充分的评估实验表明柯西分布能更良地表征图像小波系数,因此利用柯西分布来表征小波系数,并在此基础上利用率失真理论推导有损压缩用的最优量化步长。实验仿真结果表明,所提出的加密图像压缩算法能获得良好的压缩效率和重构质量,且能显著优于同类经典算法,并与常规的JPEG压缩算法性能相当甚至更好。

How to compress encrypted data effectively and reconstruct it in a high quality way is a challenging research problem in the cloud computing environment.The challenge mainly comes from the encryption by cloud users,which masks statistical characteristics of cover data and thus makes the compression of encrypted data in the cloud side unable to fully exploit the statistical characteristics in a traditional way.Based on the fitness evaluation of statistical models in characterizing wavelet coefficients,a new compression scheme is proposed on encrypted grey images,which leverages the statistic model and rate distortion optimization.The coarsest subband and the other wavelet subbands generated through lifting wavelet decomposition of grey image are encrypted by stream and permutation ciphers,respectively.They are then compressed in lossless and lossy ways,respectively.The receiver finally performs the inverse operations to reconstruct the original image.As sufficient tests show that the Cauchy distribution can well characterize wavelet coefficients,the Cauchy distribution is adopt to represent wavelet subbands.Via this statistical model,the rate-distortion theory is further used to derive optimal quantization steps for lossy compression.Experimental results show that the proposed algorithm can obtain better compressing efficiency and reconstruction quality.Also,it is significantly better than other permutation-based prior arts,and it is comparable to or even better than the conventional JPEG compression algorithm.