Towards Deep Learning Methods for Quality Assessment of Computer-Generated Imagery
This addresses the need for better quality assessment in video gaming streaming services, but it is incremental as it builds on existing methods and datasets.
The paper tackles the problem of assessing quality in computer-generated imagery like video games, where existing metrics underperform, by proposing a deep learning-based metric and showing that training on 5k images with Xception modules yields relatively high performance.
Video gaming streaming services are growing rapidly due to new services such as passive video streaming, e.g. Twitch.tv, and cloud gaming, e.g. Nvidia Geforce Now. In contrast to traditional video content, gaming content has special characteristics such as extremely high motion for some games, special motion patterns, synthetic content and repetitive content, which makes the state-of-the-art video and image quality metrics perform weaker for this special computer generated content. In this paper, we outline our plan to build a deep learningbased quality metric for video gaming quality assessment. In addition, we present initial results by training the network based on VMAF values as a ground truth to give some insights on how to build a metric in future. The paper describes the method that is used to choose an appropriate Convolutional Neural Network architecture. Furthermore, we estimate the size of the required subjective quality dataset which achieves a sufficiently high performance. The results show that by taking around 5k images for training of the last six modules of Xception, we can obtain a relatively high performance metric to assess the quality of distorted video games.