In-Capture Mobile Video Distortions: A Study of Subjective Behavior and Objective Algorithms

Digital videos often contain visual distortions that are introduced by the camera's hardware or processing software during the capture process. These distortions often detract from a viewer's quality of experience. Understanding how human observers perceive the visual quality of digital vi...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 28; no. 9; pp. 2061 - 2077
Main Authors: Ghadiyaram, Deepti, Pan, Janice, Bovik, Alan C., Moorthy, Anush Krishna, Panda, Prasanjit, Yang, Kai-Chieh
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Cameras
Computer simulation
Distortion
Electronic devices
in-capture distortions
Judgments
Mobile communication
Mobile handsets
Mobile videos
Quality
Quality assessment
smart-phone cameras
Streaming media
subjective quality assessment
Vibration
Video compression
Visual observation
Visual perception driven algorithms
ISSN:1051-8215, 1558-2205
Online Access:Get full text
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Summary:Digital videos often contain visual distortions that are introduced by the camera's hardware or processing software during the capture process. These distortions often detract from a viewer's quality of experience. Understanding how human observers perceive the visual quality of digital videos is of great importance to camera designers. Thus, the development of automatic objective methods that accurately quantify the impact of visual distortions on perception has greatly accelerated. Video quality algorithm design and verification require realistic databases of distorted videos and human judgments of them. However, most current publicly available video quality databases have been created under highly controlled conditions using graded, simulated, and post-capture distortions (such as jitter and compression artifacts) on high-quality videos. The commercial plethora of hand-held mobile video capture devices produces videos often afflicted by a variety of complex distortions generated during the capturing process. These in-capture distortions are not well-modeled by the synthetic, post-capture distortions found in existing VQA databases. Toward overcoming this limitation, we designed and created a new database that we call the LIVE-Qualcomm mobile in-capture video quality database, comprising a total of 208 videos, which model six common in-capture distortions. We also conducted a subjective quality assessment study using this database, in which each video was assessed by 39 unique subjects. Furthermore, we evaluated several top-performing no-reference IQA and VQA algorithms on the new database and studied how real-world in-capture distortions challenge both human viewers as well as automatic perceptual quality prediction models. The new database is freely available at: http://live.ece.utexas.edu/research/incaptureDatabase/index.html .
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2017.2707479