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

Abstract

Existing works in the field of quality assessment focus separately on gaming and non-gaming content. Along with the traditional modeling approaches, deep learning based approaches have been used to develop quality models, due to their high prediction accuracy. 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. The light-weight, low complexity nature of the model makes it suitable for real-time applications considering both gaming and non-gaming content while achieving similar performance to existing state-of-the-art model NDNetGaming. The model implementation for testing is available on GitHub1.