Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Toeplitz-Structured Compressed Sensing Matrices
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
A compressive sensing approach to object-based surveillance video coding
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
Distributed video coding using compressive sampling
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Reweighted compressive sampling for image compression
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Perceptual compressive sensing for image signals
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A compressive sensing approach for progressive transmission of images
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Image representation by compressive sensing for visual sensor networks
Journal of Visual Communication and Image Representation
Block compressed sensing of images using directional transforms
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Model-based compressive sensing
IEEE Transactions on Information Theory
Compressive Sampling with Coefficients Random Permutations for Image Compression
CMSP '11 Proceedings of the 2011 International Conference on Multimedia and Signal Processing - Volume 01
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
SIAM Journal on Imaging Sciences
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
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The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.