基于马尔可夫随机场的加密二值图像有损压缩算法

尽管当前已有众多二值图像的压缩方法,但这些方法并不能直接应用于加密二值图像的压缩。在云计算、分布式处理等场景下,如何高效地对加密二值图像进行有损压缩仍然是一个挑战,而当前鲜有这方面的研究。针对此问题,提出了一种基于马尔可夫随机场(MRF)的加密二值图像有损压缩算法。该算法用MRF表征二值图像的空域统计特性,进而借助MRF及解压缩还原的像素推断加密二值图像压缩过程中被丢弃的像素。所提算法的发送方采用流密码对二值图像进行加密,云端先后利用分块均匀但块内随机的下抽样方式及低密度奇偶校验(LDPC)编码对加密二值图像进行压缩,接收方则通过构造包含解码、解密及MRF重构的联合因子图实现二值图像的有损重构。实验结果表明,所提算法获得了较好的压缩效率,在0.2~0.4 bpp压缩率时有损重构图像的比特误差率(BER)不超过5%;而与针对未加密原始二值图像的国际压缩标准JBIG2的压缩效率相比,所提算法的压缩效率与其相当。这些充分表明了所提算法的可行性与有效性。

Lossy compression algorithm for encrypted binary images using Markov random field

Although nowadays there are many compression methods of binary images, they cannot be directly applied to compress encrypted binary images. In scenarios like cloud computing and distributed computing, how to perform lossy compression efficiently on encrypted binary images remains a challenge, and there are few researches focusing on it. Aiming at this problem, a lossy compression algorithm for encrypted binary images using Markov Random Field (MRF) were proposed. MRF was used to characterize the spatial statistics of binary image, and MRF as well as the decompressed pixels was used to deduce those pixels discarded in the compression process of encrypted binary image. In the proposed algorithm, the stream cipher was used by the sender to encrypt the binary image, the subsampling method with uniform blocks and random in the block and Low-Density Parity-Check (LDPC)-based encoding were employed by the cloud server to compress the encrypted binary image, and the joint factor graph including the decoding, decryption and MRF-based reconstruction was constructed by the receiver to realize the lossy reconstruction of the binary image. The experimental results show that the proposed algorithm achieves desirable compression efficiency with the Bit Error Rate (BER) of the lossy reconstructed binary image smaller than 5% when compression rate is 0.2 to 0.4 bpp (bit per pixel). When compared with the compression efficiency of the international compression standard JBIG2 (Joint Bi-level Image experts Group version 2) of original unencrypted binary images, the proposed algorithm obtains the comparable compression efficiency. These fully demonstrate the feasibility and effectiveness of the proposed algorithm.