A compressive sensing approach to object-based surveillance video coding

Statistically Matched Wavelet Based Texture Synthesis in a Compressive Sensing Framework

“…As can be seen the proposed scheme is more efficient than the existing schemes. The main difference between a conventional wavelet based compression scheme in a CS framework (DWT-CS) [21,35,45] and our approach is the use of Statistically Matched Wavelet based sparsification [15,43], as opposed to generic DWT used in previous works. The existing schemes use regular DWT that is not able to fully exploit the signal properties (orthogonal/nonredundant DWT cannot well preserve the regularity and/or periodicity [46]) and hence do not fully exploit the sparsity.…”

“…Measurements from Noiselet have been chosen because they are highly incoherent with the considered sparse domain and RIP tends to hold for reasonable values of . In addition, noiselet comes with very fast algorithms and just like the Fourier Table 3: Illustrating the PSNR and MOS data of the proposed model ((a) floor box, (b) black hole, (c) escalator, and (d) brick wall) using JPEG2000, conventional wavelet based CS scheme (DWT-CS) [21,35], and our proposed CS scheme (SMWT-CS). transform, the noiselet matrix does not need to be stored to be applied to a vector.…”

“…Bit mapper block is responsible for generating a final bit stream encompassing encoded data from standard codecs for approximation subspace and CS measurements : Synthesis results of structural texture with pseudoperiodic patterns (brick wall and escalator textures). For each original texture, the lower image pair is the synthesized texture using conventional DWT-based CS scheme for image synthesis [21,35], and the upper image pair is the synthesized texture using our proposed scheme. for detail subspace.…”

“…Compressive sensing framework opens a new research dimension in which most of the sparse signals can be reconstructed from a small number of measurements ( ), using algorithms like convex optimization, greedy methods, and iterative thresholding [17,18]. Significant theoretical contributions have been published on the compressive sensing in recent years [16,17,19] for image processing applications [20][21][22][23][24]. Compressive sensing framework mainly consists of three stages, that is, sparsification by transformation, measurement (projection), and optimization (reconstruction).…”

Video Forensic of Fragmented Video Based on H.264/AVC Video Compression Standard

“…As a consequence, the authentication and validation of a given content have become more and more difficult, due to the possible diverse origins and the potential alterations that could have been operated. At the same time, a significant research effort has been recently devoted to the forensic analysis of multimedia data [1][2][3]. A large part of the research activities in this field are devoted to the analysis of still images.…”