Dictionary based surveillance image compression |
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Affiliation: | 1. Siberian Federal University, pr. Svobodnyi 79/10, Krasnoyarsk, 660041, Russia;2. Krasnoyarsk Institute of Railway Transport, Irkutsk State University of Railways, ul. Lado Ketskhoveli 89, Krasnoyarsk, 660028, Russia;3. A.A. Trofimuk Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, pr. Akademika Koptyuga 3, Novosibirsk, 630090, Russia;4. GeoTekhMonitoring, ul. Baumana 3, Krasnoyarsk, 660028, Russia;5. Geodizond, pr. Gagarina 14, St. Petersburg, 196211, Russia;6. Novosibirsk State University, ul. Pirogova 2, Novosibirsk, 630090, Russia |
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Abstract: | Common image compression techniques suitable for general purpose may be less effective for such specific applications as video surveillance. Since a stationed surveillance camera always targets at a fixed scene, its captured images exhibit high consistency in content or structure. In this paper, we propose a surveillance image compression technique via dictionary learning to fully exploit the constant characteristics of a target scene. This method transforms images over sparsely tailored over-complete dictionaries learned directly from image samples rather than a fixed one, and thus can approximate an image with fewer coefficients. A set of dictionaries trained off-line is applied for sparse representation. An adaptive image blocking method is developed so that the encoder can represent an image in a texture-aware way. Experimental results show that the proposed algorithm significantly outperforms JPEG and JPEG 2000 in terms of both quality of reconstructed images and compression ratio as well. |
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Keywords: | Video surveillance Image compression Dictionary learning Sparse representation DCT transform Still images JPEG JPEG2000 |
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