In modern-era video streaming systems, videos are streamed and displayed on a wide range of devices. Such devices vary from large-screen UHD and HDTVs to medium-screen Desktop PCs and Laptops to smaller-screen devices such as mobile phones and tablets. It is well known that a video is perceived differently when displayed on different devices. The viewing experience for a particular video on smaller screen devices such as smartphones and tablets, which have high pixel density, will be different with respect to the case where the same video is played on a large screen device such as a TV or PC monitor. Being able to model such relative differences in perception effectively can help in the design of better quality metrics and in the design of more efficient and optimized encoding profiles, leading to lower storage, encoding, and transmission costs. This paper presents a new, open-source dataset consisting of subjective ratings for various encoded video sequences of different resolutions and bitrates (quality) when viewed on three devices of varying screen sizes: TV, Tablet, and Mobile. Along with the subjective scores, an evaluation of some of the most famous and commonly used open-source objective quality metrics is also presented. It is observed that the performance of the metrics varies a lot across different device types, with the recently standardized ITU-T P.1204.3 Model, on average, outperforming their full-reference counterparts. The dataset consisting of the videos, along with their subjective and objective scores, is available freely on Github at https://github.com/NabajeetBarman/Multiscreen-Dataset.

翻译：现代视频流系统中，视频内容被流式传输并在各类设备上播放。这些设备涵盖大屏幕超高清电视和高清电视、中等屏幕的台式电脑和笔记本电脑，以及小屏幕设备（如手机和平板电脑）。众所周知，同一视频在不同设备上播放时，用户的感知存在差异。对于智能手机和平板电脑等高像素密度的小屏幕设备，与电视或电脑显示器等大屏幕设备相比，观看特定视频的体验会有所不同。有效建模此类感知差异有助于设计更优的质量评估指标，以及更高效、更优化的编码配置文件，进而降低存储、编码和传输成本。本文提出一个全新的开源数据集，包含在不同分辨率与比特率（质量）下编码的视频序列，在三种尺寸屏幕设备（电视、平板电脑和手机）上观看时的主观评分。除主观评分外，本文还对当前最常用且知名的开源客观质量指标进行了评估。结果表明，不同设备类型下各指标的性能差异显著，其中新近标准化的ITU-T P.1204.3模型在平均性能上优于全参考类指标。该数据集（含视频内容及其主观与客观评分）已在GitHub上开源发布，地址为：https://github.com/NabajeetBarman/Multiscreen-Dataset。