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IPAD: Intensity potential for adaptive de-quantization

机译:IPAD:自适应反量化的强度潜力

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Display devices at bit-depth of 10 or higher have been mature but the mainstream media source is still at bit-depth as low as 8. To accommodate the gap, the most economic solution is to render source at low bit-depth for high bit-depth display, which is essentially the procedure of de-quantization. Traditional methods, like zero-padding or bit replication, introduce annoying false contour artifacts. To better estimate the least-significant bits, later works use filtering or interpolation approaches, which exploit only limited neighbor information, can not thoroughly remove the false contours. In this paper, we propose a novel intensity potential field to model the complicated relationships among pixels. Then, an adaptive de-quantization algorithm is proposed to convert low bit-depth images to high bit-depth ones. To the best of our knowledge, this is the first attempt to apply potential field for natural images. The proposed potential field preserves local consistency and models the complicated contexts very well. Extensive experiments on natural image datasets validate the efficiency of the proposed intensity potential field. Significant improvements have been achieved over the state-of-the-art methods on both PSNR and SSIM.
机译:比特深度为10或更高的显示设备已经成熟,但主流媒体源的比特深度仍然低至8。为了弥补这一差距,最经济的解决方案是将低比特深度的源渲染为高比特。深度显示,这实质上是反量化的过程。传统方法(例如零填充或位复制)会引入令人讨厌的错误轮廓伪影。为了更好地估计最低有效位,以后的工作使用过滤或插值方法,这些方法仅利用有限的邻居信息,无法彻底消除错误轮廓。在本文中,我们提出了一种新颖的强度势场来模拟像素之间的复杂关系。然后,提出了一种自适应去量化算法,将低位深度的图像转换为高位深度的图像。据我们所知,这是首次尝试将势场应用于自然图像。拟议的潜在领域保留了局部一致性,并很好地模拟了复杂的环境。在自然图像数据集上进行的大量实验验证了所提出的强度势场的效率。相对于PSNR和SSIM的最新方法,已经实现了重大改进。

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