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Resolution Progressive Compression of Encrypted Images

Remya S and Dilshad Rasheed V A
MES College of Engineering, Kuttippuram,Kerala,India
Abstract—The image compression algorithms are used to reduce the memory space or transmission time. This image compression algorithms can be combined with cryptography to keep the security of the data. Once the data are decrypted, all secrets will be leaked. Hence compression is performed after encryption of the image if there is a need to transmit the redundant data over an insecure and bandwidth constrained channel. The reverse system is also used now a days and it ensures more security. In both cases there is no compromise in compression efficiency. For compression and encryption several techniques are used. Some of these techniques are applicable to grayscale images, some others to color images and others do both. To encrypt the compressed data and for transformation function many techniques are used in several areas. For this transform based coding is used. Resolution Progressive Compression (RPC) is an efficient method for compressing encrypted gray scale images and is based on DWT. In this method at the encoder side a down sampled version of the cipher text is created and an intraframe prediction is performed and it is sent to the decoder. This is a low resolution image and the decoder decodes and decrypts this image and get a high resolution version. This predicted image together with the encryption key is known as Side Information (SI). This SI is used as the input for the next resolution level. The decoder is Slepian Wolf decoder and works by avoiding the Markovian property. Thus the complexity of coding and decoding can be reduced. This resolution progressive compression performs best result for grayscale and color images.

Index Terms—slepian wolf coding, RPC

Cite: Remya S and Dilshad Rasheed V A, "Resolution Progressive Compression of Encrypted Images," International Journal of Signal Processing Systems, Vol. 1, No. 1, pp. 7-10,  June 2013. doi: 10.12720/ijsps.1.1.7-10
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