Is It Possible to Predict Human Perception of Video Quality? The Assessment of Sencogi Quality Metric Maria Laura Mele 1,2(B) , Silvia Colabrese 1 , Luca Calabria 1 , and Christiaan Erik Rijnders 1 1 COGISEN Srl, Rome, Italy {marialaura,silvia,luca,chris}@cogisen.com 2 Department of Philosophy, Social and Human Sciences and Education, University of Perugia, Perugia, Italy Abstract. Sencogi Quality Metric (SenQM) is a novel objective metric for video quality assessment. SenQM infers video quality scores from the spatio-temporal evolution of videos. The quality model behind SenQM is based on an algorithm developed by Cogisen for modelling dynamic phenomena generated by complex systems. In the field of video compression, Cogisen’s algorithm uses machine learning to model human perception of video quality by extracting meaningful information directly from the video data domain and its frequency representation. The model has been trained over datasets of (i) x264 compressed videos as input data and (ii) the corresponding subjective Mean Opinion Scores as ground truth. This study introduces the model behind SenQM and how the proposed metric performs in subjective video quality prediction compared to the most used video quality assessment methods, i.e. PSNR, SSIM, and Netflix’s VMAF. Results indi- cate a significantly higher prediction performance in terms of monotonicity, con- sistency, and accuracy than the compared metrics. SenQM quality scores show significantly higher variations for 352 × 288 resolution videos with equivalent levels of degradation, and outstands PSNR, SSIM, and VMAF in predicting sub- jective scores of increasing levels of compression without being affected by either the degradation level or the video content. Keywords: Machine learning · Video quality assessment · Objective video quality 1 Introduction Today, the effectiveness of video service providers is strictly related to how they meet the video quality expectations of their users. Subjective video quality plays an important role in affecting the user’s quality of experience. The Video Quality Assessment (VQA) methodology evaluates the quality of a video as perceived by an average human observer either by subjective or objective methods. © Springer Nature Switzerland AG 2020 C. Stephanidis et al. (Eds.): HCII 2020, LNCS 12423, pp. 234–247, 2020. https://doi.org/10.1007/978-3-030-60114-0_16