Abstract
3D textured models are an integral part of modern computer graphics. The geometry of these models is represented by a 3D polygonal mesh and the textures by 2D images. 3D textured models occupy more storage space than conventional images, since we need to store both the mesh and the texture. Thus, they can be expensive to store and transmit. One way to reduce these costs is to compress the mesh and texture in 3D models. Compression invariably leads to the loss of visual quality. Therefore, a method for objectively measuring the perceived loss in visual quality is important. There are studies that can mathematically model the perceptual impact of 3D mesh compression. However, there are only a few studies on the perceptual impact of texture compression. In this paper, we perform a subjective experiment to measure the perceived loss of quality of a 3D model caused by JPEG compression of the model’s texture. We propose a simple modeling function that can determine the perceived quality of a 3D model with JPEG compressed texture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guilford, J.P.: Psychometric methods (1954)
Iglewicz, B., Hoaglin, D.C.: How to Detect and Handle Outliers, ASQC Basic References in Quality Control. American Society for Quality Control, Milwaukee (1993)
Larson, E.C., Chandler, D.M.: Most apparent distortion: a dual strategy for full-reference image quality assessment. In: Proceedings of SPIE, vol. 7242, pp. 72,420S–72,420S–17 (2009)
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. Sig. Process. Lett. IEEE 20(3), 209–212 (2013)
Pan, Y., Cheng, I., Basu, A.: Quality metric for approximating subjective evaluation of 3-D objects. Trans. Multimedia 7(2), 269–279 (2005). https://doi.org/10.1109/TMM.2005.843364
Rogowitz, B.E., Rushmeier, H.E.: Are image quality metrics adequate to evaluate the quality of geometric objects? vol. 4299, pp. 340–348 (2001). https://doi.org/10.1117/12.429504
Zhang, L., Zhang, D., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kottayil, N.K., Cheng, I., Punithakumar, K., Basu, A. (2018). Adapting Texture Compression to Perceptual Quality Metric for Textured 3D Models. In: Basu, A., Berretti, S. (eds) Smart Multimedia. ICSM 2018. Lecture Notes in Computer Science(), vol 11010. Springer, Cham. https://doi.org/10.1007/978-3-030-04375-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-04375-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04374-2
Online ISBN: 978-3-030-04375-9
eBook Packages: Computer ScienceComputer Science (R0)