@inproceedings{bd1392cf15e0451e8138645a4eaa8764,
title = "IPAD: Intensity potential for adaptive de-quantization",
abstract = "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.",
keywords = "Bit-depth, De-quantization, Potential field",
author = "Jing Liu and Guangtao Zhai and Xiaokang Yang and Menghan Hu and Chen, \{Chang Wen\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 ; Conference date: 10-07-2017 Through 14-07-2017",
year = "2017",
month = aug,
day = "28",
doi = "10.1109/ICME.2017.8019328",
language = "英语",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
pages = "1207--1212",
booktitle = "2017 IEEE International Conference on Multimedia and Expo, ICME 2017",
address = "美国",
}