Corpus ID: 235458621

A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis

@article{Bourbia2021AMC,
  title={A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis},
  author={Salima Bourbia and Ayoub Karine and A. Chetouani and Mohammed El Hassouni Lrit and Mohammed V Rabat and Rabat and Morocco. and Isen Yncrea Ouest and 33 Quater Chemin du Champ de Manoeuvre and 44470 Carquefou and France and Laboratoire Prisme and Universit'e d'Orl'eans and Flsh},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09303}
}
This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integrate these characteristics to estimate the quality of stereoscopic images without reference through a convolutional neural network. Our method is based on two main tasks: the first… Expand

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