An efficient approach to layered-depth image based rendering

Master'sMASTER OF SCIENC

An edge-based structural distortion indicator for the quality assessment of 3D synthesized views

International audience3D-TV applications require the generation of novel viewpoints through Depth-Image-Based-Rendering methods. These synthesized views need to be assessed by a reliable quality metric. Most of the proposed metrics are inspired from 2D commonly used quality metrics. Yet, the latter were originally designed to address 2D compression distortions which are different from the distortions related to DIBR processes. We propose an edge-based method that indicates the level of structural degradation in the synthesized image. The first results are encouraging since the correlation to subjective scores is higher than other tested metrics

View and depth preprocessing for view synthesis enhancement

In the paper, two preprocessing methods for virtual view synthesis are presented. In the first approach, both horizontal and vertical resolutions of the real views and the corresponding depth maps are doubled in order to perform view synthesis on images with densely arranged points. In the second method, real views are filtered in order to eliminate blurred or improperly shifted edges of the objects. Both methods are performed prior to synthesis, thus they may be applied to different Depth-Image-Based Rendering algorithms. In the paper, for both proposed methods, the achieved quality gains are presented

Rate-Distortion Analysis of Multiview Coding in a DIBR Framework

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