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

翻译：我们针对从印度社交平台ShareChat获取的一组移动端视频，开展了大规模主观感知质量研究。在受控实验室环境中，由志愿者观看的内容为现有用户生成内容（UGC）视频质量数据集增添了文化多样性。鉴于视觉互联网与社交媒体平台的全球爆炸性增长，特别是智能手机拍摄视频在印度等新兴经济体的快速普及，大规模、多样化的UGC视频质量评估（VQA）数据集需求极为迫切。ShareChat为用户提供了安全且具文化社区属性的空间，支持用户以偏好的印度语言及方言生成与分享内容。基于此数据开展的主观质量研究，为视频质量研究领域带来了文化、视觉与语言的多元化增益。我们期待这一新数据资源能推动系统开发，使其可预测印度社交媒体视频的感知视觉质量，用于控制流媒体缩放与压缩协议、优化用户推荐，并指导内容分析与处理。通过在此数据集上评估主流无参考视频质量模型（包括名为MoEVA的新模型——该模型采用专家混合机制预测视频质量），我们验证了新数据资源的价值。全新的LIVE-ShareChat数据集及MoEVA示例源代码现已向研究社区免费开放，获取地址为：https://github.com/sandeep-sm/LIVE-SC