Application of Optimized Adaptive Neuro-Fuzzy Inference for High Frame Rate Video Quality Assessment
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Video Quality Assessment · Advanced Image Processing Techniques · Image and Signal Denoising Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, 10000 Zagreb, Croatia
Funding
University of Zagreb
References (25)
Show source-provided references
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