Research Article Measurement of Objective Video Quality in Social Cloud Based on Reference Metric Sajida Karim , 1 Hui He , 1 A. R. Junejo, 2 and Mariyam Sattar 3 1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2 School of Control Science and Control Engineering, Harbin Institute of Technology, Harbin, China 3 Department of Mechanical Engineering, Institute of Space Technology, Islamabad, Pakistan Correspondence should be addressed to Hui He; hehui@hit.edu.cn Received 20 June 2019; Revised 24 October 2019; Accepted 26 November 2019; Published 13 January 2020 Academic Editor: Jun Cai Copyright © 2020 Sajida Karim et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is paper explores the objective of the present video quality analysis (VQA) and measures the full reference metrics keeping in view the quality degradation. During the research work, we conduct experiments on different social clouds (SCs) and low-quality videos. Selected videos are uploaded to SC to assess differences in video service and quality. WeChat shows that the average of all videos (Avg  100), peak signal-to-noise ratio (PSNR), has no impact on other indicators. erefore, we believe that WeChat provides the best video quality and multimedia services to their users to meet Quality of Service (QoS)/Quality of Experience (QoE). 1. Introduction At present, the number of social clouds (SCs) has exceeded the number of multimedia services, and the number of users joining the SC has also increased dramatically. is is be- cause video access from the cloud such as video on demand (VOD), online video gaming, streaming multimedia, In- ternet protocol television (IPTV), video conferencing, and social networking has been widely used all over the world. ese multimedia applications and services are now com- monly implemented in various fields, including scientific and educational sectors (e.g., multimedia presentations) [1, 2]. Multimedia service providers (MSPs) devise various approaches and techniques to provide a better QoE that is diversely improved by the user experiences. e users can easily access the QoS from the commercial or noncom- mercial cloud servers. erefore, the quality of multimedia services is more critical to the designs and deployed by any networks and services [3]. Users have always high expectations and demands for qualities of video. During the video transmission and online streaming, a single video may experience jitter or additional noise by uploading/downloading of videos on the cloud because the SC compresses the original video and reduces the storage size that automatically affects the video quality [4, 5]. e unnecessary noise increases the problem for MSP and reduces the provision of high definition (HD), high- quality video services agreeing to the user demands. However, QoE and QoS are two modalities in multimedia societies and are highly interconnected to ensure the reli- ability of quality assessment (QA) that promises to provide QoS and improve the user QoE. Generally, QoE refers to the user factors, i.e., enjoyment, feelings, and satisfaction, while QoS refers to the performance of multimedia applications and networks, e.g., performance, responsiveness, and availability. Quality of Experience is further subcategorized into subjective and objective QoE. Subjective QoE is carried out by surveys (e.g., scale rate, interviews, and question- naires). Objective QoE, on the other hand, carries out hu- man physiological tests (e.g., MRI and EEG) and measures QoS data or technical parameters (e.g., cost, resolution, frame, and sampling rates) [4–6]. Hindawi Wireless Communications and Mobile Computing Volume 2020, Article ID 5028132, 13 pages https://doi.org/10.1155/2020/5028132