Low-complexity automated depth-order estimation for 2D-to-3D video conversion

Robert Klepko

doi:10.1117/12.906231

15 February 2012 Low-complexity automated depth-order estimation for 2D-to-3D video conversion

Robert Klepko

Proceedings Volume 8305, Visual Information Processing and Communication III; 83050D (2012) https://doi.org/10.1117/12.906231
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

The increasing popularity of 3D TV creates the desire for more 3D video content. Unfortunately, it will take much time for there to be an abundance of 3D video content derived from stereoscopic cameras. However, there currently exists a vast quantity of 2D video material that can potentially be converted to 3D. Converting 2D into 3D is a complex process, and so can be costly. Thus, an automated solution that can be achieved with low-complexity would be desirable. Our past research work has already resulted in a real-time 2D-to-3D conversion technique, but this generates a surrogate depth map that results in pseudo-3D and not necessarily accurate 3D. Our current research focuses on improving the accuracy of the 3D effect by implementing a technique composed of a multi-step process to determine the depth-order of objects, with respect to the camera, in each frame of a video sequence, and incorporating into our existing technique. The multi-step process can be summarized as follows: detect pixels that belong to an edge; use block-based motion estimation to determine if an edge pixel is moving and thus belongs to a moving edge (i.e., occlusion boundary); determine which of either the left or right side block moves with the moving edge pixel, and by deduction determines the occluding object; select seed points from the moving edge pixels; implement color-only region growing from each seed; cluster regions into objects based on their proximity; globally assign depth-order to the objects based on perceived viewing perspective of a frame; and modify the original surrogate depth map to create a more accurate depth map. Test results show that this is a very effective and fast technique for deriving the depth-order of objects and generating more accurate depth map values.

Citation Download Citation

Robert Klepko "Low-complexity automated depth-order estimation for 2D-to-3D video conversion", Proc. SPIE 8305, Visual Information Processing and Communication III, 83050D (15 February 2012); https://doi.org/10.1117/12.906231

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KEYWORDS

Video

Motion estimation

Image segmentation

Video processing

Cameras

Motion measurement

Edge detection

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