Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment
Video content and streaming services have become integral to modern networks, driving increases in data traffic and necessitating effective methods for evaluating Quality of Experience (QoE). Accurately measuring QoE is critical for ensuring user satisfaction in multimedia applications. In this stud...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/5018 |
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| author | Marko Matulin Štefica Mrvelj Marko Periša Ivan Grgurević |
| author_facet | Marko Matulin Štefica Mrvelj Marko Periša Ivan Grgurević |
| author_sort | Marko Matulin |
| collection | DOAJ |
| description | Video content and streaming services have become integral to modern networks, driving increases in data traffic and necessitating effective methods for evaluating Quality of Experience (QoE). Accurately measuring QoE is critical for ensuring user satisfaction in multimedia applications. In this study, an optimized adaptive neuro-fuzzy inference model that leverages subtractive clustering for high frame rate video quality assessment is presented. The model was developed and validated using the publicly available LIVE-YT-HFR dataset, which comprises 480 high-frame-rate video sequences and quality ratings provided by 85 subjects. The subtractive clustering parameters were optimized to strike a balance between model complexity and predictive accuracy. A targeted evaluation against the LIVE-YT-HFR subjective ratings yielded a root mean squared error of 2.9091, a Pearson correlation of 0.9174, and a Spearman rank-order correlation of 0.9048, underscoring the model’s superior accuracy compared to existing methods. |
| format | Article |
| id | doaj-art-d2b810d4b113428d9b834bdccb8d3be8 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d2b810d4b113428d9b834bdccb8d3be82025-08-20T02:58:47ZengMDPI AGApplied Sciences2076-34172025-04-01159501810.3390/app15095018Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality AssessmentMarko Matulin0Štefica Mrvelj1Marko Periša2Ivan Grgurević3University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, CroatiaUniversity of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, CroatiaUniversity of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, CroatiaUniversity of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, CroatiaVideo content and streaming services have become integral to modern networks, driving increases in data traffic and necessitating effective methods for evaluating Quality of Experience (QoE). Accurately measuring QoE is critical for ensuring user satisfaction in multimedia applications. In this study, an optimized adaptive neuro-fuzzy inference model that leverages subtractive clustering for high frame rate video quality assessment is presented. The model was developed and validated using the publicly available LIVE-YT-HFR dataset, which comprises 480 high-frame-rate video sequences and quality ratings provided by 85 subjects. The subtractive clustering parameters were optimized to strike a balance between model complexity and predictive accuracy. A targeted evaluation against the LIVE-YT-HFR subjective ratings yielded a root mean squared error of 2.9091, a Pearson correlation of 0.9174, and a Spearman rank-order correlation of 0.9048, underscoring the model’s superior accuracy compared to existing methods.https://www.mdpi.com/2076-3417/15/9/5018video qualityvideo streaminghigh frame rateevaluationquality of experiencemultimedia applications |
| spellingShingle | Marko Matulin Štefica Mrvelj Marko Periša Ivan Grgurević Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment Applied Sciences video quality video streaming high frame rate evaluation quality of experience multimedia applications |
| title | Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment |
| title_full | Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment |
| title_fullStr | Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment |
| title_full_unstemmed | Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment |
| title_short | Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment |
| title_sort | application of optimized adaptive neuro fuzzy inference for high frame rate video quality assessment |
| topic | video quality video streaming high frame rate evaluation quality of experience multimedia applications |
| url | https://www.mdpi.com/2076-3417/15/9/5018 |
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