Objective Video Quality Assessment Method for Object Recognition Tasks

Author:

Leszczuk Mikołaj1ORCID,Janowski Lucjan1ORCID,Nawała Jakub2ORCID,Boev Atanas3

Affiliation:

1. AGH University of Krakow, al. Adama Mickiewicza 30, 30-059 Kraków, Poland

2. Department of Electrical Electronic Engineering, University of Bristol, Bristol BS8 1QU, UK

3. Huawei Technologies Dusseldorf GmbH, 40549 Düsseldorf, Germany

Abstract

In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine.

Funder

Huawei Technologies

Publisher

MDPI AG

Reference30 articles.

1. Leszczuk, M., Janowski, L., Nawała, J., Zhu, J., Wang, Y., and Boev, A. (2023). Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition. Electronics, 12.

2. ITU-T Study Group 12 (2023). LS about New Work Item P.Obj-Recognition: Object-Recognition-Rate-Estimation Model in Surveillance Video of Autonomous Driving, 2023, ITU-T Study Group 12. Ref.: SG12-TD311.

3. NTT (2023). Draft Terms of Reference (ToR) P.obj-recog, Contribution SG12-Cn; International Telecommunication Union.

4. NTT (2023). Draft Test Plan of P.obj-recog, Contribution SG12-Cn; International Telecommunication Union.

5. Leszczuk, M., Janowski, L., Nawała, J., and Boev, A. (2022). Objective video quality assessment method for face recognition tasks. Electronics, 11.

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