Compressed sensing is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this work, we consider video systems where acquisition is carried out in the form of direct compressive sampling with no other form of sophisticated encoding. Therefore, the burden of quality video sequence reconstruction falls solely on the receiver side. We propose effective implicit motion compensation at the receiver/decoder via iterative sparsity-aware recovery on adaptively forward-backward estimated Karhunen-Loève transform (KLT) bases. We also develop sliding-window based inter-frame decoding that adaptively estimates KLT bases from adjacent previously reconstructed frames to enhance the sparse representation of each video frame block. In addition, we propose a frame-by-frame CS video encoder that performs intra-frame encoding, and exploits inter-frame similarities at the decoder via spatial-temporal total variation (TV) minimization. To further reduce the encoding complexity, we suggest and develop a block-level CS video system with inter-frame TV minimization decoding. We show that rate-distortion performance in such a block-level video system can be dramatically enhanced via adaptive CS sampling rate allocation. Experimental results included in this work illustrate the presented developments.
|Advisor:||Pados, Dimitris A.|
|Commitee:||Batalama, Stella N., Su, Weifeng|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||DAI-B 74/02(E), Dissertation Abstracts International|
|Keywords:||Compressed sensing, Compressive sampling, Motion estimation, Sparse representation, Video codecs, Video streaming|
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