1. A Study of Conventional and Learning-Based Depth Estimators for Immersive Video Transmission
- Author
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Lingadahalli Ravi, Smitha, Milovanovic, Marta, Morin, Luce, Henry, Félix, Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA), Orange Labs [Cesson-Sévigné], Orange Labs, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie, and Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)
- Subjects
view synthesis ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,depth estimation ,deep learning ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,[INFO]Computer Science [cs] ,MPEG-I ,immersive video transmission - Abstract
International audience; Obtaining an accurate depth map of a scene is very important for major applications like immersive video, robotics, autonomous driving, and many more. The different methods to estimate depths can be classified as conventional and learning-based methods. While these methods have been studied for their depth accuracy, less attention has been paid to studying their performance in the use case of depth image-based rendering (DIBR). Here we study and evaluate two conventional methods and five learning-based methods for a real-world use case of immersive video transmission in the context of MPEG-I. The user-requested views are synthesized using Test Model for Immersive Video (TMIV) from the depth maps obtained by all methods and original texture views. The synthesized images are compared with their original counterparts using various quality metrics.
- Published
- 2022