Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

TL;DR

This paper presents a rate-distortion framework for optimal bit allocation between texture and depth data in multiview video coding, enabling efficient view synthesis with reduced complexity and independence from inpainting methods.

Contribution

It introduces a simplified, accurate model for depth and texture images that allows for effective bit allocation without additional rendering or dependence on inpainting techniques.

Findings

Model accurately predicts rate-distortion behavior for depth and texture images.

Proposed bit allocation strategy improves coding efficiency in multiview scenarios.

Experimental results confirm theoretical predictions and effectiveness of the method.

Abstract

Depth image based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. Encoding rate control has thus to consider both texture and depth data. Due to different structures of depth and texture images and their different roles on the rendered views, distributing the available bit budget between them however requires a careful analysis. Information loss due to texture coding affects the value of pixels in synthesized views while errors in depth information lead to shift in objects or unexpected patterns at their boundaries. In this paper, we address the problem of efficient bit allocation between textures and depth data of multiview video sequences. We adopt a rate-distortion framework based on a simplified model of depth and texture images. Our model preserves the main features of depth and texture images. Unlike most recent solutions, our method permits to avoid rendering at encoding time for distortion estimation so that the encoding complexity is not augmented. In addition to this, our model is independent of the underlying inpainting method that is used at decoder. Experiments confirm our theoretical results and the efficiency of our rate allocation strategy.