A no-reference image and video visual quality metric based on machine learning

Vladimir Frantc; Viacheslav Voronin; Evgenii Semenishchev; Maxim Minkin; Aliy Delov

doi:10.1117/12.2309517

13 April 2018 A no-reference image and video visual quality metric based on machine learning

Vladimir Frantc, Viacheslav Voronin, Evgenii Semenishchev, Maxim Minkin, Aliy Delov

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961Y (2018) https://doi.org/10.1117/12.2309517
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

The paper presents a novel visual quality metric for lossy compressed video quality assessment. 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 EVVQ dataset with comparison existing approaches.

Citation Download Citation

Vladimir Frantc, Viacheslav Voronin, Evgenii Semenishchev, Maxim Minkin, and Aliy Delov "A no-reference image and video visual quality metric based on machine learning", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961Y (13 April 2018); https://doi.org/10.1117/12.2309517

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