Graph-based representation for multiview image coding

TL;DR

This paper introduces a graph-based multiview image representation that efficiently encodes geometry information, leading to improved reconstruction quality over traditional depth-based methods.

Contribution

It presents a novel graph-based representation for multiview images that adapts geometry accuracy based on prediction complexity, enhancing coding efficiency.

Findings

Achieves 2 dB gain in reconstructed quality compared to depth-based schemes.

Provides a compact and adaptable geometry representation for multiview image coding.

Demonstrates the effectiveness of graph-based encoding in experimental tests.

Abstract

In this paper, we propose a new representation for multiview image sets. Our approach relies on graphs to describe geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in the 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. This multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our GBR adapts the accuracy of the geometry representation, in contrast with depth coding, which directly compresses with losses the original geometry signal. We present the principles of this graph-based representation (GBR) and we build a complete prototype coding scheme for multiview images. Experimental results demonstrate the potential of this new representation as compared to a depth-based approach. GBR can achieve a gain of 2 dB in reconstructed quality over depth-based schemes operating at similar rates.