Subjective and objective quality assessment of Mobile Videos with In-Capture distortions
Deepti Ghadiyaram, Janice Pan, Alan Conrad Bovik, Anush Krishna Moorthy, Prasanjit Panda, Kai‐Chieh Yang · 2017
We designed and created a new video database that models a variety of complex distortions generated during the video capturing process on hand-held mobile capturing devices. We describe the content and characteristics of the new database, which we call the LIVE Mobile In-Capture Video Quality Database. It comprises a total of 208 videos that were captured using eight different smart-phones and were affected by six common in-capture distortions. We also conducted a subjective video quality assessment study using this data, wherein each video was assessed by 36 unique subjects. We evaluated several top-performing No-Reference IQA and VQA algorithms on the new database and find insights on how real-world in-capture distortions challenge both human subjects as well as automatic perceptual quality prediction models.