Focus mismatch compensation and complexity reduction techniques for multiview video coding

UnrestrictedMultiview video systems utilize multiple cameras to simultaneously capture the scene from different viewpoints. They provide video data for new applications such as 3D television and free-viewpoint video. The amount of data in multiview video is very large as compared to monoscopic video. Multiview video coding (MVC) is an emerging research field that focuses on compression of multiview video data. In this dissertation, by exploiting special characteristics of multiview video, we develop techniques that improve MVC efficiency while also taking complexity into account.; First, we analyze focus mismatch exhibited in video content, which is caused by camera focus setting differences. We use geometrical optics to demonstrate how focus settings will affect the captured images. We show that the focus mismatch can be represented in terms of the focus setting parameters (camera-dependency) and the depths of objects (depth-dependency). For 1D parallel camera arrangements in multiview systems, we relate the focus mismatch to the disparity exhibited in frames from different views. The analytical results provide properties that can be exploited to design focus mismatch compensation techniques.; Based on this analysis, we propose a novel adaptive reference filtering (ARF) approach. For MVC inter-view prediction, we exploit the depth-dependency property by utilizing disparity information to partition frames into depth levels, which are prone to suffer from different types of focus mismatch. For each level, a 2D filter is designed by minimizing the prediction error. Filtered references are then generated for predictive coding. We also extend ARF to monoscopic video where no disparity information is available. Simulation results demonstrate higher coding efficiency as compared to multiple-reference prediction and adaptive interpolation filtering methods.; The third part of this thesis presents complexity reduction techniques for MVC. By analyzing ARF results, we propose i) View-wise ARF adaptation based on RD-cost prediction, and ii) Filter updating based on depth-composition change, to achieve computationally efficient ARF schemes. By exploiting the relationship between motion and disparity, we propose predictive fast search algorithms that can be used when one of the fields is available and we wish to estimate the other field efficiently. Simulation results show that significant complexity reduction can be achieved without significant impact on coding efficiency