Stereoscopic Image Quality Assessment Based on both Distortion and Disparity

Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.

show abstract

Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

Alvi

Farid

Khan

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Stereoscopic Image Quality Assessment Based on both Distortion and Disparity

Cited by 1 publication

References 17 publications

Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

Contact Info

Product

Resources

About