Sparse Representations-based depth images quality assessment

• Main Authors: Dorsaf Sebai, Maryem Sehli, Faouzi Ghorbel
• Format: Article
• Language: English
• Published: Elsevier 2021-03-01
• Series: Visual Informatics
• Subjects: Depth maps; Sparse representations; Transform domain; Image Quality Assessment; 3D-HEVC
• Online Access: http://www.sciencedirect.com/science/article/pii/S2468502X21000061

The conventional 2D metrics can be used for measuring the quality of depth maps, but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality. In this paper, we propose a new full reference objective metric, called Sparse Representations-Mean Squared Error (SR-MSE), which efficiently evaluates the depth maps compression distortions. It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features. Then, it computes the mean squared error between the sparse coefficients issued from this modeling. As a benchmark of quality assessment, we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates. We compare the subjective results with the proposed and conventional objective metrics. Experimental results demonstrate that the proposed SR-MSE, compared to the conventional image quality assessment metrics, yields the highest correlated scores to the subjective ones.