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» Learning Compressible Models
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ICIP
2006
IEEE
15 years 11 months ago
Error Inhomogeneity of Wavelet Image Compression
Despite the popularity of wavelet-based image compression, its error inhomogeneity - the error that is different for even and odd pixel locations, has not been previously analyzed...
Naixiang Lian, Vitali Zagorodnov, Yap-Peng Tan
ICASSP
2009
IEEE
15 years 4 months ago
CMOS compressed imaging by Random Convolution
We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configurat...
Laurent Jacques, Pierre Vandergheynst, Alexandre B...
SIGGRAPH
2000
ACM
15 years 2 months ago
Progressive geometry compression
We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist o...
Andrei Khodakovsky, Peter Schröder, Wim Sweld...
EUROCRYPT
2006
Springer
15 years 1 months ago
How to Strengthen Pseudo-random Generators by Using Compression
Sequence compression is one of the most promising tools for strengthening pseudo-random generators used in stream ciphers. Indeed, adding compression components can thwart algebrai...
Aline Gouget, Hervé Sibert
ICASSP
2010
IEEE
14 years 10 months ago
Kronecker product matrices for compressive sensing
Compressive sensing (CS) is an emerging approach for acquisition of signals having a sparse or compressible representation in some basis. While CS literature has mostly focused on...
Marco F. Duarte, Richard G. Baraniuk