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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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 21; no. 7; pp. 3206 - 3219
Main Authors: Thirumalai, V., Frossard, P.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.07.2012
Institute of Electrical and Electronics Engineers
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2012.2188035