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Proceedings Paper

GPU-aided motion adaptive video deinterlacing
Author(s): Xiaolin Wu; Jie Cao
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Paper Abstract

In most applications, video deinterlacing has to be performed in real time. Numerous algorithms have been developed to strike a good balance between throughput and quality. The motion adaptive deinterlacing algorithm switches between two modes: direct merging of two fields in areas of no motion, or intrafield adaptive interpolation when motions are detected. In this paper, we propose a fast GPU-aided implementation of a motion adaptive deinterlacing algorithm using NVIDIA CUDA (Compute Unified Device Architecture) technology. We discuss the techniques of adapting the computations in motion detection and adaptive directional interpolation to the GPU architecture for maximum video throughput possible. The objective is to fully utilize the processing power of GPU without compromising the visual quality of the deinterlaced video. Experimental results are reported and discussed to demonstrate the performance of the proposed GPU-aided motion adaptive video deinterlacer in both speed and visual quality.

Paper Details

Date Published: 18 January 2010
PDF: 10 pages
Proc. SPIE 7543, Visual Information Processing and Communication, 754308 (18 January 2010); doi: 10.1117/12.846786
Show Author Affiliations
Xiaolin Wu, McMaster Univ. (Canada)
Jie Cao, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 7543:
Visual Information Processing and Communication
Amir Said; Onur G. Guleryuz, Editor(s)

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