NSS-MDL: Natural Scene Statistics-guided multi-task deep learning for no-reference point cloud quality assessment
Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni, Maher Jridi · Intelligent Systems with Applications · 2025
The increasing use of 3D point clouds in fields like virtual reality, robotics, and 3D gaming has made quality assessment a critical and essential task. Many no-reference point cloud quality assessment (NR-PCQA) methods fail to capture the critical relationship between geometric and color features, limiting their accuracy, and lacking their generalization capabilities. To address these challenges, we propose NSS-MDL, a NR-PCQA framework that integrates Natural Scene Statistics (NSS) into a multi-task deep learning architecture. The model is trained with two complementary tasks: the main task predicts the perceptual quality score, while the auxiliary task estimates NSS features. The main contribution of this work lies in the use of NSS estimation as an auxiliary task to enhance the capacity of deep learning-based models to represent both the naturalness and the degradation of point clouds, leading to more accurate and robust quality predictions Experimental evaluations on two large benchmark datasets, WPC and SJTU, demonstrate that NSS-MDL outperforms state-of-the-art methods in terms of correlation with subjective quality scores. The results highlight the robustness and generalizability of the proposed method across diverse datasets. The code of the NSS-MDL model will soon be publicly available on https://github.com/Salima-Bourbia/NSS-MDL .