Joint Reconstruction of Multi-view Compressed Images

TL;DR

This paper proposes a joint reconstruction method for multi-view compressed images that leverages correlation models to improve image quality over independent decoding, outperforming existing schemes.

Contribution

It introduces a convex optimization framework for joint decoding of independently compressed multi-view images using correlation estimation.

Findings

Joint reconstruction outperforms independent decoding in image quality.

The method surpasses state-of-the-art distributed coding schemes.

Reconstruction quality improves at a given bit rate.

Abstract

The distributed representation of correlated multi-view images is an important problem that arise in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed correlated images are jointly decoded in order to improve the reconstruction quality of all the compressed images. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among different cameras. A central decoder first estimates the underlying correlation model from the independently compressed images which will be used for the joint signal recovery. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images that comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be consistent with their compressed versions. We show by 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 proposed algorithm compares advantageously to state-of-the-art distributed coding schemes based on disparity learning and on the DISCOVER.