On the performance of 3D just noticeable difference models

Yu Fan, M. Larabi, F. A. Cheikh, C. Fernandez-Maloigne
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引用次数: 4

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.
就3D性能而言,模型之间的差别只是显而易见的
刚可注意差分(JND)概念反映了最大可容忍失真。它已被广泛用于二维应用程序的优化。对于立体3D (S3D)内容,这个概念是不同的,因为它依赖于与我们的双目视觉相关的不同机制。与2D模型不同,3D-JND模型是最近才出现的,相关文献相当有限。这些模型可用于S3D内容的压缩和质量评价改进。在本文中,我们对现有的3D-JND模型进行了深入的比较研究。此外,为了分析其性能,3D-JND模型已被集成到最近专门用于立体图像质量评估(SIQA)的度量中。结果报告了两个广泛使用的S3D图像数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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