SAL-360IQA: A Saliency Weighted Patch-Based CNN Model for 360-Degree Images Quality Assessment

Abstract

Since the introduction of 360-degree images, a significant number of deep learning based image quality assessment (IQA) models have been introduced. Most of them are based on multichannel architectures where several convolutional neural networks (CNNs) are used together. Despite the competitive results, these models come with a higher cost in terms of complexity. To significantly reduce the complexity and ease the training of the CNN model, this paper proposes a patch-based scheme dedicated to 360-degree IQA. Our framework is developed including patches selection and extraction based on latitude to account for the importance of the equatorial region, data normalization, CNN-based architecture and a weighted average pooling of predicted local qualities. We evaluate the proposed model on two widely used databases and show the superiority to state-of-the-art models, even multichannel ones. Furthermore, the cross-database assessment revealed the good generalization ability, demonstrating the robustness of the proposed model.