Opinion-unaware blind stereoscopic image quality assessment: A comprehensive study

The development of blind Stereoscopic Image Quality Assessment (SIQA) is hindered by the limited availability of large-scale datasets. Existing SIQA databases typically contain only a few hundred images which are often derived from a small number of original sources. This data scarcity poses a significant challenge, particularly in the deep learning era, as it increases the risk of overfitting. Moreover, it makes performance comparisons of different blind SIQA models unreliable when using publicly available databases. Consequently, determining the best-performing model remains difficult under current evaluation methodologies. To address this limitation and advance SIQA research, we construct the largest and most diverse SIQA database to date which incorporates both image-level coarse labels and single-view pseudo labels. Utilizing this extensive dataset, we have conducted comprehensive study on blind SIQA models, exploring variations in network architecture, input size and auxiliary supervision signals. The representational capabilities of various blind SIQA models and their variants are systematically evaluated under consistent training conditions, specifically pairwise opinion-unaware learning. This new benchmark provides a more reliable platform for comparing blind SIQA models, enabling fairer and more comprehensive assessments of their relative strengths and limitations.