Compressive sensing in block-based image/video coding

Bing Han; Jun Xu; Dapeng Wu; Jun Tian

doi:10.1117/12.849167

28 April 2010 Compressive sensing in block based image/video coding

Bing Han, Jun Xu, Dapeng Wu, Jun Tian

Proceedings Volume 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010; 77080R (2010) https://doi.org/10.1117/12.849167
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Bing Han, Jun Xu, Dapeng Wu, and Jun Tian "Compressive sensing in block based image/video coding", Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 77080R (28 April 2010); https://doi.org/10.1117/12.849167

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KEYWORDS

Image compression

Quantization

Compressed sensing

Image restoration

Computer programming

Detection theory

Optimization (mathematics)

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