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Depth-based 3D videos: quality measurement and synthesized view enhancement

Three dimensional television (3DTV) is believed to be the future of television broadcasting that will replace current 2D HDTV technology. In the future, 3DTV will bring a more life-like and visually immersive home entertainment experience, in which users will have the freedom to navigate through the scene to choose a different viewpoint. A desired view can be synthesized at the receiver side using depth image-based rendering (DIBR). While this approach has many advantages, one of the key challenges in DIBR is generating high quality synthesized views. This work presents novel methods to measure and enhance the quality of 3D videos generated through DIBR. 
For quality measurements we describe a novel method to characterize and measure distortions by multiple cameras used to capture stereoscopic images. In addition, we present an objective quality measure for DIBR-based 3D videos by evaluating the elements of visual discomfort in stereoscopic 3D videos. We also introduce a new concept called the ideal depth estimate, and define the tools to estimate that depth. Full-reference and no-reference profiles for calculating the proposed measures are also presented. 
Moreover, we introduce two innovative approaches to improve the quality of the synthesized views generated by DIBR. The first approach is based on hierarchical blending of the background and foreground information around the disocclusion areas which produces a natural looking, synthesized view with seamless hole-filling. This approach yields virtual images that are free of any geometric distortions, unlike other algorithms that preprocess the depth map. In contrast to the other hole-filling approaches, our approach is not sensitive to depth maps with high percentage of bad pixels from stereo matching. The second approach further enhances the results through a depth-adaptive preprocessing of the colored images. 
Finally, we propose an enhancement over depth estimation algorithm using the depth monocular cues from luminance and chrominance. The estimated depth will be evaluated using our quality measure, and the hole-filling algorithm will be used to generate synthesized views. This application will demonstrate how our quality measures and enhancement algorithms could help in the development of high quality stereoscopic depth-based synthesized videos.