Joint Reconstruction of Multi-view Compressed Images
This work addresses the challenge of efficiently reconstructing correlated multi-view images in distributed vision systems, offering incremental improvements over existing methods.
The paper tackles the problem of improving reconstruction quality for multi-view images compressed independently in vision sensor networks by jointly decoding them using a constrained convex optimization approach that estimates and enforces correlation models. Experimental results show the proposed scheme outperforms independent reconstruction and compares favorably to state-of-the-art distributed coding methods in terms of image quality at a given bit rate.
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.