  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 task predicts naturalness
analysis based features adapted to stereo images, while the second task
predicts the quality of such images. The former, so-called auxiliary task, aims
to find more robust and relevant features to improve the quality prediction. To
do this, we compute naturalness-based features using a Natural Scene Statistics
(NSS) model in the complex wavelet domain. It allows to capture the statistical
dependency between pairs of the stereoscopic images. Experiments are conducted
on the well known LIVE PHASE I and LIVE PHASE II databases. The results
obtained show the relevance of our method when comparing with those of the
state-of-the-art. Our code is available online on
https://github.com/Bourbia-Salima/multitask-cnn-nrsiqa_2021.
