161685
20190316235009.0
REP_WORK
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
2010
2010
17
Reports
The 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.
Random projections, sparse approximations, motion estimation, disparity estimation, consistent reconstruction,LTS4
Thirumalai, Vijayaraghavan
171237
242952
Frossard, Pascal
101475
241061
Preprint
381694
n/a
http://infoscience.epfl.ch/record/161685/files/TIP2010-CS-SI.pdf
LTS4
252393
U10851
oai:infoscience.tind.io:161685
report
STI
GLOBAL_SET
171237
171237
171237
171237
EPFL-REPORT-161685
EPFL
PUBLISHED
REPORT