An experimental survey of no-reference video quality assessment methods

• Published in: International journal of pervasive computing and communications Vol. 12; no. 1; pp. 66 - 86
• Main Authors: Maria Torres Vega, Sguazzo, Vittorio, Decebal Constantin Mocanu, Liotta, Antonio
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 01.01.2016
• Subjects: Accuracy; Algorithms; Benchmarks; Cameras; Computer simulation; Digital media; Quality; Quality assessment; Streams; Studies; Video compression; Video data
• ISSN: 1742-7371; 1742-738X
• DOI: 10.1108/IJPCC-01-2016-0008

PurposeThe Video Quality Metric (VQM) is one of the most used objective methods to assess video quality, because of its high correlation with the human visual system (HVS). VQM is, however, not viable in real-time deployments such as mobile streaming, not only due to its high computational demands but also because, as a Full Reference (FR) metric, it requires both the original video and its impaired counterpart. In contrast, No Reference (NR) objective algorithms operate directly on the impaired video and are considerably faster but loose out in accuracy. The purpose of this paper is to study how differently NR metrics perform in the presence of network impairments.Design/methodology/approachThe authors assess eight NR metrics, alongside a lightweight FR metric, using VQM as benchmark in a self-developed network-impaired video data set. This paper covers a range of methods, a diverse set of video types and encoding conditions and a variety of network impairment test-cases.FindingsThe authors show the extent by which packet loss affects different video types, correlating the accuracy of NR metrics to the FR benchmark. This paper helps identifying the conditions under which simple metrics may be used effectively and indicates an avenue to control the quality of streaming systems.Originality/valueMost studies in literature have focused on assessing streams that are either unaffected by the network (e.g. looking at the effects of video compression algorithms) or are affected by synthetic network impairments (i.e. via simulated network conditions). The authors show that when streams are affected by real network conditions, assessing Quality of Experience becomes even harder, as the existing metrics perform poorly.