Distributed compressive video sensing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Distributed video coding using compressive sampling
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Distributed compressed video sensing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Residual Reconstruction for Block-Based Compressed Sensing of Video
DCC '11 Proceedings of the 2011 Data Compression Conference
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Dictionary Learning for Stereo Image Representation
IEEE Transactions on Image Processing
Correlation Noise Modeling for Efficient Pixel and Transform Domain Wyner–Ziv Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
Maximum Frame Rate Video Acquisition Using Adaptive Compressed Sensing
IEEE Transactions on Circuits and Systems for Video Technology
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Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient reconstruction by leveraging more realistic signal models that go beyond simple sparsity is still an open challenge. In this paper, we propose a novel ''undersampled'' correlation noise model to describe compressively sampled video signals, and present a maximum-likelihood dictionary learning based reconstruction algorithm for DCVS, in which both the correlation and sparsity constraints are included in a new probabilistic model. Moreover, the signal recovery in our algorithm is performed during the process of dictionary learning, instead of being employed as an independent task. Experimental results show that our proposal compares favorably with other existing methods, with 0.1-3.5dB improvements in the average PSNR, and a 2-9dB gain for non-key frames when key frames are subsampled at an increased rate.