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16 May 2018 Aggregating motion cues and image quality metrics for video quality estimation
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The interest in computer vision has grown at a profound rate in recent years. This interest has brought numerous state-of-the-art approaches to all aspects of computer vision, from object detection and recognition in still imagery to action recognition and navigation in driverless vehicles. However, when applied to DoD-relevant real-world data, these approaches struggle to produce the quality of results seen in academic datasets. To assist with the issue of dealing with real-world data, video quality assessment algorithms are often used to understand the difficulty of a particular dataset and may provide guidance to algorithm selection, bandwidth requirements, and other information pertinent to the automatic analysis of imagery and video. In this work we study the aggregation of motion estimation on video frames and image quality metrics on still frames for the automatic assessment of video quality. We study several state-of-the-art optical flow algorithms as well as commonly used image quality algorithms and methods to combine the two to form an aggregate video quality score. We test our approach on real-world videos and compare the combined results to the original scores to study the efficacy of our approach.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Josh Harguess and Michael Reese "Aggregating motion cues and image quality metrics for video quality estimation", Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450A (16 May 2018);

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