000161685 001__ 161685
000161685 005__ 20180925134126.0
000161685 037__ $$aREP_WORK
000161685 245__ $$aDistributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
000161685 269__ $$a2010
000161685 260__ $$c2010
000161685 300__ $$a17
000161685 336__ $$aReports
000161685 520__ $$aThe distributed representation of correlated images is an important challenge in applications such as multi-view imaging in camera networks or low complexity video coding. This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or the positioning of the vision sensors. It concentrates on the problem where images are encoded with compressed linear measurements, which are used for estimation of the correlation between images at decoder. We propose a geometry-based correlation model in order to describe the correlation between pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. These prominent visual features are estimated with a sparse approximation of a reference image by a dictionary of geometric basis functions. The corresponding features in the other images are then identified from the compressed measurements. The correlation model is given by the relative geometric transformations between corresponding features. We thus formulate a regularized optimization problem for the estimation of correspondences where the local transformations between images form a consistent motion or disparity map. Then, we propose an efficient joint reconstruction algorithm that decodes the compressed images such that they stay consistent with the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in video sequences or multi-view data. In addition, the proposed reconstruction strategy provides effective decoding performance that compares advantageously to distributed coding schemes based on disparity or motion learning and to independent coding solution based on JPEG-2000.
000161685 6531_ $$aRandom projections, sparse approximations, motion estimation, disparity estimation, consistent reconstruction,LTS4
000161685 700__ $$0242952$$aThirumalai, Vijayaraghavan$$g171237
000161685 700__ $$0241061$$aFrossard, Pascal$$g101475
000161685 8564_ $$s381694$$uhttps://infoscience.epfl.ch/record/161685/files/TIP2010-CS-SI.pdf$$yPreprint$$zn/a
000161685 909C0 $$0252393$$pLTS4$$xU10851
000161685 909CO $$ooai:infoscience.tind.io:161685$$pSTI$$preport
000161685 917Z8 $$x171237
000161685 917Z8 $$x171237
000161685 917Z8 $$x171237
000161685 917Z8 $$x171237
000161685 937__ $$aEPFL-REPORT-161685
000161685 973__ $$aEPFL$$sPUBLISHED
000161685 980__ $$aREPORT