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 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 requires 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, 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. Towards 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, where 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.