Published December 16, 2020 | Version v1
Conference paper Open

DEMI: Deep Video Quality Estimation Model using Perceptual Video Quality Dimensions

  • 1. TU Berlin, Germany
  • 2. Kingston University, London, UK
  • 3. Technische Universität Ilmenau, Germany


In this paper, we present a deep learning-based quality estimation model considering both gaming and non-gaming videos. The model is developed in three phases. First, a convolutional neural network (CNN) is trained based on an objective metric which allows the CNN to learn video artifacts such as blurriness and blockiness. Next, the model is fine-tuned based on a small image quality dataset using blockiness and blurriness ratings. Finally, a Random Forest is used to pool frame-level predictions and temporal information of videos in order to predict the overall video quality.



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ACCORDION – Adaptive edge/cloud compute and network continuum over a heterogeneous sparse edge infrastructure to support nextgen applications 871793
European Commission