On the performance of 3D just noticeable difference models

Abstract : The just noticeable difference (JND) notion reflects the maximum tolerable distortion. It has been extensively used for the optimization of 2D applications. For stereoscopic 3D (S3D) content, this notion is different since it relies on different mechanisms linked to our binocular vision. Unlike 2D, 3D-JND models appeared recently and the related literature is rather limited. These models can be used for the sake of compression and quality assessment improvement for S3D content. In this paper, we propose a deep and comparative study of the existing 3D-JND models. Additionally, in order to analyze their performance, the 3D-JND models have been integrated in recent metric dedicated to stereoscopic image quality assessment (SIQA). The results are reported on two widely used S3D image databases.
Type de document :
Communication dans un congrès
IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States. pp.1017-1021, 2016, 〈10.1109/ICIP.2016.7532511〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01405748
Contributeur : Mohamed-Chaker Larabi <>
Soumis le : mercredi 30 novembre 2016 - 13:26:16
Dernière modification le : jeudi 8 décembre 2016 - 15:30:11

Identifiants

Collections

Citation

Yu Fan, Mohamed-Chaker Larabi, Cheikh Faouzi Alaya, Christine Fernandez-Maloigne. On the performance of 3D just noticeable difference models. IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States. pp.1017-1021, 2016, 〈10.1109/ICIP.2016.7532511〉. 〈hal-01405748〉

Partager

Métriques

Consultations de la notice

85