A new perceptual evaluation method of video quality based on neural network

Article type: Research Article

Authors: Frnda, Jaroslav^{a; *} | Pavlicko, Michal^a | Durica, Marek^a | Sevcik, Lukas^b | Voznak, Miroslav^{b; c} | Fournier-Viger, Philippe^d | Lin, Jerry Chun-Wei^e

Affiliations: [a] Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovakia | [b] IT4Innovations, VSB-Technical University of Ostrava, Ostrava, Czech Republic | [c] Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic | [d] School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), University Town, Shenzhen, Guangdong, China | [e] Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

Correspondence: [*] Corresponding author: Jaroslav Frnda, Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia. E-mail: jaroslav.frnda@fpedas.uniza.sk.

Abstract: This paper proposes a novel method for video quality evaluation based on machine learning technique. The current research deals with the correct interpretation of objective video quality evaluation (Quality of Service – QoS) in relation to subjective end-user perception (Quality of Experience – QoE), typically expressed by mean opinion score (MOS). Our method allows us to interconnect results obtained from video objective and subjective assessment methods in the form of a neural network (computing model inspired by biological neural networks). So far, no unified interpretation scale has been standardized for both approaches, therefore it is difficult to determine the level of end-user satisfaction obtained from the objective assessment. Thus, contribution of the proposed method lies in description of the way to create a hybrid metric that delivers fast and reliable subjective score of perceived video quality for internet television (IPTV) broadcasting companies.

Keywords: ACR, neural network, SSIM, QoE, QoS, video assessment methods

DOI: 10.3233/IDA-205085

Journal: Intelligent Data Analysis, vol. 25, no. 3, pp. 571-587, 2021