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    A 3D Perceptual Metric using Just-Noticeable-Difference
    (The Eurographics Association, 2005) Cheng, Irene; Boulanger, Pierre; John Dingliana and Fabio Ganovelli
    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.
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    A Visual Quality Prediction Model for 3D Texture
    (The Eurographics Association, 2005) Cheng, Irene; Boulanger, Pierre; John Dingliana and Fabio Ganovelli
    Online bandwidth limitations and fluctuations impose a major challenge in estimating the amount of data to transmit in a given time period. Over or under estimation of bandwidth can jeopardize the visual fidelity of the transmitted and related multimedia data. We propose a Visual Quality Prediction (VQP) model which supports an adaptive fragmented texture transmission approach taking bandwidth fluctuations into consideration, and adjusts the data size (and thus quality) of the next block of texture data to be transmitted. The transmission depends on a set of predictors used to optimize an overall best effort visual quality.