A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis
This work addresses the problem of assessing stereoscopic image quality without reference for applications in 3D imaging and vision, but it appears incremental as it builds on existing methods with specific adaptations.
The paper tackles blind stereoscopic image quality assessment by proposing a multi-task convolutional neural network that integrates naturalness analysis and quality prediction, achieving results that show relevance compared to state-of-the-art methods on LIVE PHASE I and II databases.
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.