While not all of these papers connect directly to mobile-first applications, their implications for mobile ML are significant. They push forward ML tasks commonly performed on mobile and edge devices, so their advancement is crucial in pushing the industry forward.
Perceptual Quality Assessment of Smartphone Photography
Authors of this paper performed an in-depth study of the perceptual quality assessment of smartphone photography. They also introduced the Smartphone Photography Attribute and Quality (SPAQ) database. The database contains 11,125 pictures captured by 66 smartphones. Each of the images has rich annotations.
CVPR 2020 Open Access Repository
Perceptual Quality Assessment of Smartphone Photography Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, Zhou Wang ; The…
openaccess.thecvf.com
The authors also collected human opinions for each image. Some of the information collected includes image quality, image attributes, image attributes, and scene categories labels. For deeper analysis, they also recorded the exchangeable image file format (EXIF) for each image. They then used the database to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks. The results obtained give insights into:
how EXIF data, image attributes, and high-level semantics interact with image quality
how next-generation BIQA models can be designed
how better computational photography systems can be optimized on mobile devices

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