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Proceedings Paper

Compressive sensing in block-based image/video coding
Author(s): Bing Han; Jun Xu; Dapeng Wu; Jun Tian
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Paper Abstract

Recently, Compressive Sensing (CS) has emerged as a more efficient sampling method for sparse signals. Comparing to the traditional Nyquist-Shannon sampling theory, CS provides a great reduction of sampling rate, power consumption, and computational complexity to acquire and represent sparse signals. In this paper, we propose a new block based image/video compression scheme, which uses CS to improve coding efficiency. In the traditional lossy coding schemes, such as JPEG and H.264, the dominant coding error comes from scalar quantization. The CS recovery procedure can help mitigating the quantization error in the decoding process. We use rate distortion optimization (RDO) for mode selection (MS) between the traditional inverse DCT transform and projection onto convex sets (POCS) algorithm. In our experiment, the new image compression method is able to achieve up to 1 dB gain over standard JPEG.

Paper Details

Date Published: 28 April 2010
PDF: 8 pages
Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 77080R (28 April 2010); doi: 10.1117/12.849167
Show Author Affiliations
Bing Han, Univ. of Florida Gainesville (United States)
Jun Xu, Univ. of Florida Gainesville (United States)
Dapeng Wu, Univ. of Florida Gainesville (United States)
Jun Tian, Futurewei Technologies (United States)

Published in SPIE Proceedings Vol. 7708:
Mobile Multimedia/Image Processing, Security, and Applications 2010
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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