Joint Image Compression and Encryption Based on Compressed Sensing and Entropy Coding

In this paper, we propose a joint image compression and encryption technique based on Compressed Sensing (CS). Our work is based on JPEG standard. After the quantization stage, blocks in the DCT domain become sparse, however, if we use CS on the whole block the Entropy coding compression is severely affected. As a result, we split the DCT coefficients between Entropy coding path and CS path followed by its Entropy coder. As an added value for CS, partial encryption is achieved. To split the DCT coefficients to Entropy and CS paths, we developed five different algorithms; each algorithm uses a unique method to split every 8x8 block in the image into two parts. First part is encoded using Huffman coding, while the second part goes through compression stage using CS, then encoded using Huffman coding. At the end, we combine encoded parts to get the compressed and encrypted image. To strengthen the image encryption, we use the CS secret key to shuffle image blocks using Arnold Cat Map. The five algorithms are tested on 15 popular images and the results show that 2 techniques can achieve compression gain over JPEG as well as the partial encryption, all at a PSNR very close to JPEG.