A 3D Perceptual Metric using Just-Noticeable-Difference

Abstract

In multimedia applications, it is essential to distribute resources efficiently among different types of data in order to optimize overall quality. We propose a perceptual metric using Just-Noticeable-Difference (JND) to identify redundant mesh data so that available bandwidth can be allocated to improve texture resolution. Evaluation of perceptual impact during runtime is based on statistics in a lookup table generated during preprocessing. If the impact is less than the JND, no mesh refinement is performed. We apply Weber s fraction to compute the JND threshold, which is verified by perceptual evaluations. Experimental result shows that our JND model can accurately predict perceptual impact based on the human visual system.