Quality assessment with deep learning for imaging applications
Viacheslav Voronin, Alexander A. Zelensky, Marina M. Zhdanova, Evgeny A. Semenishchev, Vladimir Frantc, Aleksey Siryakov · 2022
To establish stable video operations and services while maintaining high quality of experience, perceptual video quality assessment becomes an essential research topic in video technology. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. The paper presents a novel visual quality metric for video quality assessment. To address this problem, we study the of neural networks through the robust optimization. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the MCL-V dataset with comparison existing approaches.