Video-based dynamic mesh compression is being developed for a standard for compressing time-varying mesh data by using existing 2D video codecs. In this paper, we propose a spatial scalable structure for video-based dynamic mesh compression to support scalable applications of the dynamic mesh coding. To support the spatial scalable functionality for geometry, we propose a level of detail based displacement frame packing method that enables decoding up to a certain level. For texture scalable coding, multi-layer texture map is generated from high resolution texture map, and then the scalable high efficiency video coding (SHVC) is employed to remove redundancy between layers. The experimental results demonstrate that our proposed scalable coding method can improve the BD-rate in a 3-layer scalable configuration.

This paper presents an efficient method for adjusting the 3D depth of an object including as much as a whole scene in stereo 3D images by utilizing a virtual fronto-parallel planar projection in the 3D space perceived by a viewer. The proposed method just needs to establish object correspondence instead of the accurate estimation of the disparity field or point correspondence. We simulate the depth adjustment of a 3D point perceived by a viewer through a corresponding pair of points on the stereo 3D images by moving the virtual fronto-parallel plane on which the left and right points are projected. We show that the resulting transformation of image coordinates of the points can be simply expressed by three values of a scale factor and two translations that depend on one parameter for the depth adjustment. The experimental results demonstrate the feasibility of the proposed approach that yields less visual fatigue and smaller 3D shape distortion than the conventional parallax adjustment method. The overall procedure can be efficiently applied to each frame of a stereo video without causing any artifact.

This paper presents an adaptive parallax control method based on 3D scene change detection for stereoscopic video sequences. For each pair of left and right frames, the maximum disparity corresponding to the closest object is estimated by using a simple block matching algorithm. The 3D scene change is detected if there exists an abrupt change in the values of either the maximum disparity or color histogram between successive pair of frames. Then, if the maximum disparity is excessive at a pair of frames where the scene change has occurred, the horizontal parallax is controlled by reducing the excessive maximum disparity that could induce the visual discomfort. The experimental results demonstrate the feasibility of the proposed approach.