CHAPTER 5 – Distributed Coding of Sparse Signals

[1]  Ruby J Pai.  Nonadaptive lossy encoding of sparse signals , 2006 .

[2]  Balas K. Natarajan,et al.  Sparse Approximate Solutions to Linear Systems , 1995, SIAM J. Comput..

[3]  Sundeep Rangan,et al.  On the Rate-Distortion Performance of Compressed Sensing , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[4]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[5]  Harish Viswanathan,et al.  On the whiteness of high-resolution quantization errors , 2000, IEEE Trans. Inf. Theory.

[6]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[7]  Martin Vetterli,et al.  Rate-distortion analysis of spike processes , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[8]  D. Donoho,et al.  Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.

[9]  Vivek K Goyal,et al.  Quantized Frame Expansions with Erasures , 2001 .

[10]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[11]  Vivek K. Goyal,et al.  Multiple description coding: compression meets the network , 2001, IEEE Signal Process. Mag..

[12]  D. Donoho,et al.  Uncertainty principles and signal recovery , 1989 .

[13]  Sundeep Rangan,et al.  Recursive consistent estimation with bounded noise , 2001, IEEE Trans. Inf. Theory.

[14]  V.K. Goyal,et al.  Compressive Sampling and Lossy Compression , 2008, IEEE Signal Processing Magazine.

[15]  Robert D. Nowak,et al.  Signal Reconstruction From Noisy Random Projections , 2006, IEEE Transactions on Information Theory.

[16]  Meir Feder,et al.  On universal quantization by randomized uniform/lattice quantizers , 1992, IEEE Trans. Inf. Theory.

[17]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[18]  Vivek K. Goyal,et al.  Theoretical foundations of transform coding , 2001, IEEE Signal Process. Mag..

[19]  Ram Zamir,et al.  The rate loss in the Wyner-Ziv problem , 1996, IEEE Trans. Inf. Theory.

[20]  Martin J. Wainwright,et al.  Sharp thresholds for high-dimensional and noisy recovery of sparsity , 2006, ArXiv.

[21]  Michael Gastpar,et al.  The Distributed Karhunen–Loève Transform , 2006, IEEE Transactions on Information Theory.

[22]  Aaron D. Wyner,et al.  The rate-distortion function for source coding with side information at the decoder , 1976, IEEE Trans. Inf. Theory.

[23]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[24]  Jacob Ziv,et al.  On universal quantization , 1985, IEEE Trans. Inf. Theory.

[25]  Martin Vetterli,et al.  Data Compression and Harmonic Analysis , 1998, IEEE Trans. Inf. Theory.

[26]  Vivek K. Goyal,et al.  Quantized Overcomplete Expansions in IRN: Analysis, Synthesis, and Algorithms , 1998, IEEE Trans. Inf. Theory.

[27]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[28]  N. Meinshausen,et al.  LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA , 2008, 0806.0145.