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Updated: May 15, 2026
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Published on: June 27, 2025
Joint reconstruction of multiview compressed images
Vijayaraghavan Thirumalai1, Pascal Frossard
1Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland. vthiruma@qti.qualcomm.com
Summary
This study introduces a joint reconstruction method for correlated multiview images in vision sensor networks. The approach improves image quality by leveraging inter-view correlations during decoding, outperforming independent reconstruction.
Area of Science:
- Computer Vision
- Signal Processing
- Networked Systems
Background:
- Distributed representation of correlated multiview images is crucial for vision sensor networks.
- Independent encoding of images from different viewpoints is common, but it fails to exploit inter-view correlations.
Purpose of the Study:
- To develop a joint reconstruction method for distributively compressed multiview images.
- To enhance image quality by exploiting inter-view correlations during decoding.
Main Methods:
- A central decoder estimates inter-view image correlation from independently compressed data.
- Joint reconstruction is formulated as a constrained convex optimization problem.
- Total-variation (TV) smooth images are reconstructed, adhering to the estimated correlation model and closeness to compressed versions.
Main Results:
- The proposed joint reconstruction scheme significantly outperforms independent reconstruction in image quality for a given bit rate.
- Experimental results demonstrate superior decoding performance compared to state-of-the-art distributed coding schemes.
Conclusions:
- Joint reconstruction effectively leverages inter-view image correlation for improved quality in vision sensor networks.
- The proposed method offers a robust and efficient solution for distributed multiview image representation and decoding.
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