The role of optical flow in automated quality assessment of full-motion video

Josh Harguess; Scott Shafer; Diego Marez

doi:10.1117/12.2275392

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19 September 2017 The role of optical flow in automated quality assessment of full-motion video

Josh Harguess, Scott Shafer, Diego Marez

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Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039606 (2017) https://doi.org/10.1117/12.2275392
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

In real-world video data, such as full-motion-video (FMV) taken from unmanned vehicles, surveillance systems, and other sources, various corruptions to the raw data is inevitable. This can be due to the image acquisition process, noise, distortion, and compression artifacts, among other sources of error. However, we desire methods to analyze the quality of the video to determine whether the underlying content of the corrupted video can be analyzed by humans or machines and to what extent. Previous approaches have shown that motion estimation, or optical flow, can be an important cue in automating this video quality assessment. However, there are many di↵erent optical flow algorithms in the literature, each with their own advantages and disadvantages. We examine the e↵ect of the choice of optical flow algorithm (including baseline and state-of-the-art), on motionbased automated video quality assessment algorithms.

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Josh Harguess, Scott Shafer, and Diego Marez "The role of optical flow in automated quality assessment of full-motion video", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039606 (19 September 2017); https://doi.org/10.1117/12.2275392

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