IEEE Trans Image Process - Distributed representation of geometrically correlated images with compressed linear measurements.

Tópicos

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Resumo

This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or the camera positioning. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model that describes the common information in 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. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem in order to estimate the corresponding features in correlated images that are given by quantized linear measurements. The correlation model is thus given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that reconstructs the compressed images such that they are consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multiview data sets. In addition, the proposed algorithm provides effective decoding performance that advantageously compares to independent coding solutions and state-of-the-art distributed coding schemes based on disparity learning.

Resumo Limpo

paper address problem distribut code imag whose correl driven motion object camera posit concentr problem imag encod compress linear measur propos geometrybas correl model describ common inform pair imag assum constitut compon natur imag can captur visual featur undergo local transform eg translat differ imag first identifi promin visual featur comput spars approxim refer imag dictionari geometr basi function pose regular optim problem order estim correspond featur correl imag given quantiz linear measur correl model thus given relat geometr transform correspond featur propos effici joint decod algorithm reconstruct compress imag consist quantiz measur correl model experiment result show propos algorithm effect estim correl imag multiview data set addit propos algorithm provid effect decod perform advantag compar independ code solut stateoftheart distribut code scheme base dispar learn

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