2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MRII.4
Paper Title CONVOLUTIONAL NEURAL NETWORKS FOR OMNIDIRECTIONAL IMAGE QUALITY ASSESSMENT: PRE-TRAINED OR RE-TRAINED?
Authors Abderrezzaq Sendjasni, Université de Poitiers and NTNU, France; Mohamed-Chaker Larabi, University of Poitiers, France; Faouzi Alaya Cheikh, Norwegian University of Science and Technology, Norway
SessionSS-MRII: Special Session: Models and representations for Immersive Imaging
LocationArea A
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Models and Representations for Immersive Imaging
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Abstract The use of convolutional neural networks (CNN) for image quality assessment (IQA) becomes many researcher's focus. Various pre-trained models are fine-tuned and used for this task. In this paper, we conduct a benchmark study of seven state-of-the-art pre-trained models for IQA of omnidirectional images. To this end, we first train these models using an omnidirectional database and compare their performance with the pre-trained versions. Then, we compare the use of viewports versus equirectangular (ERP) images as inputs to the models. Finally, for the viewports-based models, we explore the impact of the input number on the models' performance. Experimental results demonstrated the performance gain of the re-trained CNNs compared to their pre-trained versions. Also, the viewports-based approach outperformed the ERP-based one independently of the number of selected views.