  We conducted a large-scale subjective study of the perceptual quality of
User-Generated Mobile Video Content on a set of mobile-originated videos
obtained from the Indian social media platform ShareChat. The content viewed by
volunteer human subjects under controlled laboratory conditions has the benefit
of culturally diversifying the existing corpus of User-Generated Content (UGC)
video quality datasets. There is a great need for large and diverse UGC-VQA
datasets, given the explosive global growth of the visual internet and social
media platforms. This is particularly true in regard to videos obtained by
smartphones, especially in rapidly emerging economies like India. ShareChat
provides a safe and cultural community oriented space for users to generate and
share content in their preferred Indian languages and dialects. Our subjective
quality study, which is based on this data, offers a boost of cultural, visual,
and language diversification to the video quality research community. We expect
that this new data resource will also allow for the development of systems that
can predict the perceived visual quality of Indian social media videos, to
control scaling and compression protocols for streaming, provide better user
recommendations, and guide content analysis and processing. We demonstrate the
value of the new data resource by conducting a study of leading blind video
quality models on it, including a new model, called MoEVA, which deploys a
mixture of experts to predict video quality. Both the new LIVE-ShareChat
dataset and sample source code for MoEVA are being made freely available to the
research community at https://github.com/sandeep-sm/LIVE-SC
