Context-aware human-centric 3D visual quality classification model Using Machine Intelligence
نویسنده
چکیده
Quality of Experience (QoE) has become an indispensable phenomenon in the ever-growing research facets of multimedia community. Context-aware QoE needs better framework to meet the demands of mobile video environments. The proliferation of various wireless technologies have endorsed wide spread usage of video applications such as 3D games and 3D movies. This work describes a novel machine learning based classification model for quality assessment in 2D and 3D videos. User-centric QoE model based on social context and location context is studied using Rapid Miner as Machine Intelligence testbed. The results demonstrate the efficiency of J48 and RBF classifiers with more than 80% accuracy.
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