Image quality is an important aspect for several applications (biometrics, tracking, object detection and so on). Several methods have been proposed in the literature to estimate it. These methods are able to predict subjective judgments according to different characteristics. The goal of this paper is to present a framework for stereoscopic image quality metric with reference based on Neural Networks (CNN & ANN). The proposed CNN model is composed of 3 convolutional layers and two Fully Connected (FC) layers and it is used to identify the degradation type in the image. The quality is then estimated using an ANN model. Its inputs are some computed features, selected according to the identified degradation type. The results obtained through two common datasets show the relevance of the proposed approach.
Aladine Chetouani, "A Neural-Based Stereoscopic Image Quality Assessment with Reference" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XV, 2018, pp 298-1 - 298-5, https://doi.org/10.2352/ISSN.2470-1173.2018.12.IQSP-298