DocumentCode :
589423
Title :
Image Coding Using Wavelet-Based Compressive Sampling
Author :
Longxu Jin ; Jin Li
Author_Institution :
Changchun Inst. of Opt., Fine Mech. & Phys., Changchun, China
Volume :
1
fYear :
2012
fDate :
28-29 Oct. 2012
Firstpage :
547
Lastpage :
550
Abstract :
In this paper, we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First, two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. after multi-scale DWT, the low-frequency sub-band and high frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients, we allocate different measurements to low-and high-frequency component. Then, the measurements samples can be quantized. the quantize samples are entropy coded and forward correct coding (FEC). Finally, the compressed streams are transmitted. at the decoder, one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.
Keywords :
codecs; discrete cosine transforms; entropy codes; image coding; image sampling; coding scheme; decoder; entropy codes; forward correct coding; image coding; multiscale DWT; statistical dependences; two-dimension discrete wavelet transform; wavelet-based CS framework; wavelet-based CS scheme; wavelet-based compressive sampling; Compressed sensing; Decoding; Discrete wavelet transforms; Image coding; Image reconstruction; Sensors; Wavelet coefficients; compressive sampling; dsidcrete cosine transform; image coding;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
Type :
conf
DOI :
10.1109/ISCID.2012.142
Filename :
6406969
Link To Document :
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