IEEE Trans Image Process - Joint reconstruction of multiview compressed images.

Tópicos

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Resumo

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.

Resumo Limpo

distribut represent correl multiview imag import problem aris vision sensor network paper concentr joint reconstruct problem distribut compress imag decod togeth order take benefit imag correl consid scenario imag captur differ viewpoint encod independ use common code solut eg jpeg balanc rate distribut among differ camera central decod first estim interview imag correl independ compress data joint reconstruct cast constrain convex optim problem reconstruct totalvari tv smooth imag compli estim correl model time add constraint forc reconstruct imag close possibl compress version show experi propos joint reconstruct scheme outperform independ reconstruct term imag qualiti given target bit rate addit decod perform algorithm compar advantag stateoftheart distribut code scheme base motion learn discov algorithm

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