| Session: | Image and Video Quality Assessment with Industry Applications |
| Location: | Lecture Room |
| Session Time: | Tuesday, June 26, 10:20 - 12:40 |
| Presentation Time: | Tuesday, June 26, 11:20 - 11:40 |
| Presentation: |
Special Session Lecture
|
| Paper Title: |
QUALITY ASSESSMENT OF THUMBNAIL AND BILLBOARD IMAGES ON MOBILE DEVICES |
| Authors: |
Zeina Sinno; The University of Texas at Austin, United States | | |
| | Anush Moorthy; Netflix, United States | | |
| | Jan De Cock; Netflix, United States | | |
| | Zhi Li; Netflix, United States | | |
| | Alan Bovik; The University of Texas at Austin, United States | | |
| Abstract: |
Objective image quality assessment (IQA) research entails developing algorithms that predict human judgments of picture quality. Validating performance entails evaluating algorithms under conditions similar to where they are deployed. Hence, creating image quality databases representative of target use cases is an important endeavor. Here we present a database that relates to quality assessment of billboard images commonly displayed on mobile devices. Billboard images are a subset of thumbnail images, that extend across a display screen, representing things like album covers, banners, or frames or artwork. We conducted a subjective study of the quality of billboard images distorted by processes like compression, scaling and chroma-subsampling, and compared high-performance quality prediction models on the images and subjective data. |