Distributed coding of multiview sparse sources with joint recovery

Huynh Van Luong, Nikos Deligiannis, Søren Forchhammer, André Kaup

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In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computational and energy constraints at each camera as well as the limitations regarding intercamera communication. Our approach addresses these challenges by exploiting the sparsity of the visual descriptor histograms as well as their intraand inter-camera correlations. Our method couples distributed source coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature transform (SIFT) descriptors extracted from multiview images shows that our method leads to bit-rate saving of up to 43% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources.
Original languageEnglish
Title of host publicationProceedings of the 32nd Picture Coding Symposium
Number of pages8
Publication date2016
Publication statusPublished - 2016
Event32nd Picture Coding Symposium - Hotel Le Meridien, Nuremberg, Germany
Duration: 4 Dec 20167 Dec 2016


Conference32nd Picture Coding Symposium
LocationHotel Le Meridien


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