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IPAD: Intensity potential for adaptive de-quantization

  • Jing Liu
  • , Guangtao Zhai
  • , Anan Liu
  • , Xiaokang Yang
  • , Xibin Zhao
  • , Chang Wen Chen
  • Tianjin University
  • Shanghai Jiao Tong University
  • Tsinghua University

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Display devices at bit depth of 10 or higher have been mature but the mainstream media source is still at bit depth of eight. 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, such as 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, cannot thoroughly remove the false contours. In this paper, we propose a novel intensity potential (IP) field to model the complicated relationships among pixels. The potential value decreases as the spatial distance to the field source increases and the potentials from different field sources are additive. Based on the proposed IP field, an adaptive de-quantization procedure is then 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 well. Extensive experiments on natural, synthetic, and high-dynamic range image data sets validate the efficiency of the proposed IP field. Significant improvements have been achieved over the state-of-the-art methods on both the peak signal-to-noise ratio and the structural similarity.

Original languageEnglish
Pages (from-to)4860-4872
Number of pages13
JournalIEEE Transactions on Image Processing
Volume27
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

  • Bit-depth
  • de-quantization
  • natural image statistics
  • potential field

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