Image Quality Assessment of 3D Synthesized Views

Abstract : Depth-Image-Based Rendering (DIBR) is a fundamental technology in several 3D-related applications, such as Free viewpoint video (FVV), Virtual Reality (VR) and Augmented Reality (AR). However, new challenges have also been brought in assessing the quality of DIBR-synthesized views since this process induces some new types of distortions, which are inherently different from the distortions caused by video coding. This work is dedicated to better evaluate the quality of DIBRsynthesized views in immersive multimedia. In chapter 2, we propose a completely No-reference (NR) metric. The principle of the first NR metrics NIQSV is to use a couple of opening and closing morphological operations to detect and measure the distortions, such as “blurry regions” and “crumbling”. In the second NR metric NIQSV+, we improve NIQSV by adding a “black hole” and a “stretching” detection. In chapter 3, we propose two Fullreference metrics to handle the geometric distortions by using a dis-occlusion mask and a multi-resolution block matching methods.In chapter 4, we present a new DIBR-synthesized image database with its associated subjective scores. This work focuses on the distortions only induced by different DIBR synthesis methods which determine the quality of experience (QoE) of these DIBR related applications. In addition, we also conduct a benchmark of the state-of-the-art objective quality assessment metrics for DIBR-synthesized views on this database. The chapter 5 concludes the contributions of this thesis and gives some directions of future work.
Complete list of metadatas

Cited literature [180 references]  Display  Hide  Download
Contributor : Abes Star <>
Submitted on : Friday, May 24, 2019 - 3:24:10 PM
Last modification on : Thursday, December 19, 2019 - 11:00:50 AM


Version validated by the jury (STAR)


  • HAL Id : tel-02139237, version 1


Shishun Tian. Image Quality Assessment of 3D Synthesized Views. Signal and Image processing. INSA de Rennes, 2019. English. ⟨NNT : 2019ISAR0002⟩. ⟨tel-02139237⟩



Record views


Files downloads