PROJECT TITLE :
Compressive Video Sampling With Approximate Message Passing Decoding
In this paper, we apply compressed sensing (CS) to video compression. CS techniques exploit the observation that one wants abundant fewer random measurements than given by the Shannon–Nyquist sampling theory to recover an object if this object is compressible (i.e., sparse in the spatial domain or in a very remodel domain). Within the CS framework, we will achieve sensing, compression, and denoising simultaneously. We propose a quick and easy on-line encoding by the appliance of pseudorandom downsampling of the two-D fast Fourier transform to video frames. For offline decoding, we have a tendency to apply a modification of the recently proposed approximate message passing (AMP) algorithm. The AMP methodology has been derived using the statistical concept of “state evolution,” and it's been shown to considerably accelerate the convergence rate in special CS-decoding applications. We have a tendency to shall prove that the AMP technique will be rewritten as a forward–backward splitting algorithm. This new illustration permits us to provide conditions that guarantee convergence of the AMP technique and to change the algorithm so as to achieve higher robustness. The success of reconstruction ways for video decoding conjointly essentially depends on the chosen transform, where sparsity of the video signals is assumed. We propose incorporating the 3-D dual-tree complicated wavelet remodel that possesses sufficiently smart directional selectivity while being computationally less expensive and less redundant than different directional three-D wavelet transforms.
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