On the practical applications of objective quality metrics for stereoscopic 3D imaging

Abstract

Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.