The high-definition video broadcasting and streaming services are blooming nowadays. Consumers can enjoy on-demand video services from Netflix, Hulu or Amazon, and watching high-definition (HD) programs becomes the mainstream for video content consumption. According to the latest report, more than half of US population watches on-line movies or dramas. Specifically, the viewers have increased from 37% in 2010 to 51% in 2013. The watched video programs vary in bit rates and resolutions due to the available bandwidth of their networks. Different sizes of video are transmitted at lower bit rates and up-scaled for display on HDTV (e.g., playing a 720p movie on the 1080p screen). This is common in people’s daily life, yet video quality assessment on HD streaming video has not yet been extensively studied in the past.
To fulfill the need, MCLab has released a new video image database recently, which is called MCL-V. This database captures two typical video distortion types in video services. It contains 12 source video clips and 96 distorted video clips with subjective assessment scores. The source video clips are selected from a large pool of public-domain HD video sequences with representative and diversified contents. Both distortion types are perceptually adjusted to yield distinguishable distortion levels. An improved pairwise comparison method is adopted for subjective evaluation. Furthermore, several existing image and video quality assessment algorithms are evaluated against MCL-V database. MCL-V is publicly accessible to facilitate future video quality research of the community.
Download link: https://mcl.usc.edu/mcl-v-database/
