Capturing and post-processing of stereoscopic 3D content for improved quality of experience

• Main Author: Xu, Di
• Language: English
• Published: University of British Columbia 2013
• Online Access: http://hdl.handle.net/2429/43913

3D video can offer real-life viewing experience by providing depth impression. 3D technology has not yet been widely adopted due to challenging 3D-related issues, ranging from capturing to post-processing and display. At the capturing side, lack of guidelines may lead to artifacts that cause viewers headaches and nausea. At the display side, not having 3D content customized to a certain aspect ratio, display size, or display technology may result in reduced quality of experience. Combining 3D with high-dynamic-range imaging technology adds exciting features towards real-life experience, whereas conventional low-dynamic-range content often suffers from color saturation distortion when shown on high-dynamic-range displays. This thesis addresses three important issues on capturing and post-processing 3D content to achieve improved quality of experience. First, we provide guidelines for capturing and displaying 3D content. We build a 3D image and video database with the content captured at various distances from the camera lenses and under different lighting conditions. We conduct comprehensive subjective tests on 3D displays of different sizes to determine the influence of these parameters to the quality of 3D images and videos before and after horizontal parallax adjustment. Next, we propose a novel and complete pipeline for automatic content-aware 3D video reframing. We develop a bottom-up 3D visual attention model that identifies the prominent regions in a 3D video frame. We further provide a dynamic bounding box that crops the video and avoids annoying problems, such as jittering and window violation. Experimental results show that our algorithm is both effective and robust. Finally, we propose two algorithms for correcting saturation in color images and videos. One algorithm uses a fast Bayesian-based approach that utilizes images’ strong spatial correlation and the correlations between the R, G, and B color channels. The other algorithm takes advantage of the strong correlation between the chroma of the saturated pixels and their surrounding unsaturated pixels. Experimental results show that our methods effectively correct the saturated 2D and 3D images and videos. Our algorithms significantly outperform the existing state-of-the-art method in both objective and subjective qualities, resulting in plausible content that resembles real-world scenes.