Distributed Representation of Geometrically Correlated Images With Compressed Linear Measurements

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 t...

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Vydané v:	IEEE transactions on image processing Ročník 21; číslo 7; s. 3206 - 3219
Hlavní autori:	Thirumalai, V., Frossard, P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.07.2012 Institute of Electrical and Electronics Engineers
Predmet:	Applied sciences Approximation methods Coding, codes Correlation Correlation estimation Decoding Dictionaries Exact sciences and technology geometric transformations Image coding Image processing Image reconstruction Information, signal and communications theory Joints quantization random projections Sampling, quantization Signal and communications theory Signal processing sparse approximations Telecommunications and information theory Performance evaluation Positioning Dictionaries State of the art random projections Image processing Optimization Image reconstruction Multiple image Learning Multi-view technology Image coding Basis function Correlation estimation Signal quantization Sparse representation quantization Geometric transformation geometric transformations Image compression Decoding Algorithm Algorithm performance Disparity sparse approximations
ISSN:	1057-7149, 1941-0042, 1941-0042
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1057-7149 1941-0042 1941-0042
DOI:	10.1109/TIP.2012.2188035