Random Forests Based View Generation for Multiview TV

Abstract

The appearance of multiview display systems in the consumer market is not far from reality. With technical knowledge in this field constantly improving, production of multiview content is the only other key factor that will determine the successful adoption of this technology. Multiview content can be generated from two or three views and their associated depth maps. Estimating a high quality depth map is challenging. Moreover transmission of depth map information requires extra bandwidth. In this study, we propose an effective algorithm, which utilizes a 3D visual attention model, multiple monocular depth cues and a fraction of depth information for estimating the whole depth map of the scene using the Random Forests (RF) machine learning algorithm. Having the estimated depth maps and stereo videos, other views may be synthesized. Performance evaluations have shown that the proposed method estimates high quality depth maps for stereo sequences from limited depth information. Implementation of our proposed technique in the future multiview pipeline eliminates the need for estimating and transmitting the whole depth map for all the views, producing high quality multiview content while reducing the required bandwidth.