IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i9p295-d1230857.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/9/295/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/9/295/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suheil Che Sobry & Muhammad Zarunnaim Haji Wahab & Mohd Zhafri Mohd Zukhi & Siti Murni Mat Khairi & Norhafizah Abdul Halim & Shazwani Mohd Salleh, 2025. "The Mediating Role of user Satisfaction on Quality of Experience and Broadband Service Adoption: A Study on Public Higher Educational Institution Users," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(8), pages 1655-1668, August.
    2. Aleksandar Đukić & Milorad K. Banjanin & Mirko Stojčić & Tihomir Đurić & Radenka Đekić & Dejan Anđelković, 2024. "An Ensemble of Machine Learning Models for the Classification and Selection of Categorical Variables in Traffic Inspection Work of Importance for the Sustainable Execution of Events," Sustainability, MDPI, vol. 16(22), pages 1-38, November.
    3. Milorad K. Banjanin & Mirko Stojčić & Đorđe Popović & Dejan Anđelković & Goran Jauševac & Maid Husić, 2025. "Classification Machine Learning Models for Enhancing the Sustainability of Postal System Modules Within the Smart Transportation Concept," Sustainability, MDPI, vol. 17(19), pages 1-22, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:295-:d:1230857. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.