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FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

Author

Listed:
  • Štefica Mrvelj

    (Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia)

  • Marko Matulin

    (Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset.

Suggested Citation

  • Štefica Mrvelj & Marko Matulin, 2023. "FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment," Future Internet, MDPI, vol. 15(9), pages 1-22, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:295-:d:1230857
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    References listed on IDEAS

    as
    1. Milorad K. Banjanin & Mirko Stojčić & Dejan Danilović & Zoran Ćurguz & Milan Vasiljević & Goran Puzić, 2022. "Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models," Sustainability, MDPI, vol. 14(24), pages 1-29, December.
    2. Ibtihal Ellawindy & Shahram Shah Heydari, 2021. "Crowdsourcing Framework for QoE-Aware SD-WAN," Future Internet, MDPI, vol. 13(8), pages 1-20, August.
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