Perceptual video quality estimation by regression with myopic experts

Giotsas, Vasileios and Deligiannis, Nikos and Fisher, Pam and Andreopoulos, Yiannis (2015) Perceptual video quality estimation by regression with myopic experts. In: 2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015. Institute of Electrical and Electronics Engineers Inc., GRC, pp. 1-6. ISBN 9781479989584

[img]
Preview
PDF (Qomex2015_giotsas)
Qomex2015_paper_v3_camera_ready.pdf - Accepted Version

Download (239kB)

Abstract

Objective video quality metrics can be viewed as 'myopic' expert systems that focus on particular aspects of visual information in video, such as image edges or motion parameters. We conjecture that the combination of many such high-level metrics leads to statistically-significant improvement in the prediction of reference-based perceptual video quality in comparison to each individual metric. To examine this hypothesis in a systematic and rigorous manner, we use: (i) the LIVE and the EPFL/PoliMi databases that provide the difference mean opinion scores (DMOS) for several video sequences under encoding and packet-loss errors; (ii) ten well-known metrics that range from mean-squared error based criteria to sophisticated visual quality estimators; (iii) five variants of regression-based supervised learning. For 400 experimental trials with random (non-overlapping) estimation and prediction subsets taken from both databases, we show that the best of our regression methods: (i) leads to statistically-significant improvement against the best individual metrics for DMOS prediction for more than 97% of the experimental trials; (ii) is statistically-equivalent to the performance of humans rating the video quality for 36.75% of the experiments with the EPFL/PoliMi database. On the contrary, no single metric achieves such statistical equivalence to human raters in any of the experimental trials.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1709
Subjects:
ID Code:
123493
Deposited By:
Deposited On:
16 Feb 2018 10:51
Refereed?:
Yes
Published?:
Published
Last Modified:
31 May 2020 00:02