Joint Reconstruction of Multiview Compressed Images

Distributed representation of correlated multiview images is an important problem that arises in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation. We...

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Vydáno v:IEEE transactions on image processing Ročník 22; číslo 5; s. 1969 - 1981
Hlavní autoři: Thirumalai, V., Frossard, P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.05.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Applied sciences
Coding
Coding, codes
Compressed
Correlation
Decoding
Depth estimation
Detection, estimation, filtering, equalization, prediction
distributed compression
Exact sciences and technology
Image coding
Image processing
Image reconstruction
Image resolution
Information, signal and communications theory
joint reconstruction
Joints
JPEG encoders-decoders
multiview images
Optimization
proximal splitting
Reconstruction
Signal and communications theory
Signal processing
Signal, noise
Studies
Telecommunications and information theory
Performance evaluation
State of the art
Image processing
Artificial vision
Information rate
Convex programming
Image reconstruction
Multiple image
Learning
proximal splitting
Image coding
optimization
Depth estimation
Signal detection
joint reconstruction
Image compression
Decoding
Algorithm
Temporal constraint
Information transmission
Constrained optimization
Image quality
Television
Image sensor
Sensor array
distributed compression
multiview images
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:Distributed representation of correlated multiview images is an important problem that arises in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG) with a balanced rate distribution among different cameras. A central decoder first estimates the inter-view image correlation from the independently compressed data. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images, which comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be as close as possible to their compressed versions. We show through experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our algorithm compares advantageously to state-of-the-art distributed coding schemes based on motion learning and on the DISCOVER algorithm.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2013.2240006