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In the present work, an effort has been made to propose and implement a new steganographic technique for images by modifying existing algorithms. This technique uses LSB steganography as the basis and randomly disperses the secret message over the entire image to ensure that the secret message cannot be obtained easily from the image. Detailed visual and statistical analysis of the algorithm reveals...
We explore the duality between lossy compression and channel coding in the operational sense: whether a capacity-achieving encoder-decoder sequence achieves the rate distortion function of the dual problem when the channel decoder [encoder] is the source compressor [decompressor, resp]. We show that, if used as a lossy compressor, the maximum-likelihood channel decoder of a randomly chosen capacity-achieving...
Steganography is the science of writing hidden messages in such a way that no one apart from the sender and intended recipient even realizes if there is any hidden message. Most of the research done before in this area is focussed on images, audios, and videos but a less amount of work has been done on MS Word documents which is identified with certain shortcomings. One of the major shortcoming in...
We propose a scheme for lossy compression of discrete memoryless sources: The compressor is the decoder of a nonlinear channel code, constructed from a sparse graph. We prove asymptotic optimality of the scheme for any separable (letter-by-letter) bounded distortion criterion. We also present a suboptimal compression algorithm, which exhibits near-optimal performance for moderate block lengths.
We present two results related to the computational complexity of lossy compression. The first result shows that for a memoryless source Ps with rate-distortion function R(D), the rate-distortion pair (R(D) + gamma, D + isin) can be achieved with constant decoding time per symbol and encoding time per symbol proportional to C1(gamma)isin-C2(gamma). The second results establishes that for any given...
We propose a scheme to implement lossy data compression for discrete equiprobable sources using block codes based on sparse matrices. We prove asymptotic optimality of the codes for a Hamming distortion criterion. We also present a sub-optimal decoding algorithm, which has near optimal performance for moderate blocklengths.
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