3D-CLuDe: A 3D Image Quality Evaluator Using Correlative Dependencies Between Luminance and Depth Attributes

Abstract

This letter presents an automatic and opinion-unaware quality assessment (QA) model for stereoscopic (3D) images affected by sensor, transmission and compression distortions. The proposed QA model does not use reference 3D images or subjective ratings from the primary dataset to map to a quality score. This design is important in 3D image quality assessment (IQA), where generalization across various 3D IQA datasets, has remained a major challenge. Therefore, to address this gap, we propose a QA model for 3D images by computing a correlation map between the luminance and depth components. Subsequently, we employ a univariate generalized Gaussian distribution (UGGD) to model the multi-scale and multi-orientation steerable decomposed subbands and capture the quality-aware representations of the computed correlation map. Then, the spread and the shape parameters of UGGD of the test 3D image are estimated, and the individual likelihood estimates are calculated from the pristine multivariate Gaussian parameters of the corresponding shape and spread features. Experiments on LIVE Phase I and Phase II datasets show that the proposed opinion-unaware model outperforms existing opinion-unaware models on Phase I and achieves competitive performance against opinion-aware models. On Phase II dataset, it delivers competitive results compared to opinion-aware and opinion-unaware approaches. The source code of the proposed model is available at: Google Drive link. © 2025 Elsevier B.V., All rights reserved.