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Compressive sensing is a potential technology for lossy image compression. With a given quality, we may represent an image with a few significant coefficients in the sparse domain. According to the sparse modeling theories, we may randomly sense a few number of measurements in a transform domain and later reconstruct the sparse representation. Typically the sensing domain is a low-complexity transform...
Compressive sensing is a potential technology for lossy image compression. With a given quality, we may represent an image with a few significant coefficients in the transform domain. When the number of the significant coefficients is much less than the number of the pixels, the assumption of sparse representation is satisfied. Based on the sparse modeling theories, an image could be sensed with a...
Compressive sensing is a signal processing technique that takes advantage of signal sparseness in some domain. To use compressive sensing, a domain in which the signal is represented as a few significant coefficients should be defined. If the proper domain is identified as a set of basis vectors, the coefficients are the projections of the signal on the basis vectors. This is typically a transformation...
This paper proposes a novel scheme that considers the data hiding with sub sampling and compressive sensing. We utilize the characteristics of compressive sensing, sparsity and random projection, to embed secret data in the observation domain of the sparse image obtained through compressive sensing. The high bit correction rate (BCR) in experiments shows the high accuracy of our proposed method.
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