Gamut-Extension Methods Considering Color Information Restoration

Masaru Takeuchi, Yusuke Sakamoto, Ryota Yokoyama, Heming Sun, Yasutaka Matsuo, Jiro Katto

Research output: Contribution to journalArticle

Abstract

Recently, ultra high definition television (UHDTV) services have become popular using satellites and the Internet. However, there are expansive volumes of high definition television (HDTV) and standard definition television (SDTV) contents held by broadcasting companies and in storage devices. Herein we propose two color space conversion (also known as gamut mapping) methods from BT.709 (used for current HDTV broadcast) to BT.2020 (used for UHDTV broadcast) that restore or estimate lost color information. One of our methods anisotropically diffuses the BT.709 chromaticities with regard to the direction of the original chromaticities in the BT.2020 color space, generating chromaticities out of BT.709 gamut. The other learns an end-to-end conversion method from a BT.709 image to a BT.2020 image and restores lost color information using convolutional neural network (CNN). Using these methods along with BT.709 images, we obtain BT.2020 images with chromaticities from the BT.709 color gamut.

Original languageEnglish
Article number8737954
Pages (from-to)80146-80158
Number of pages13
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

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Keywords

  • convolutional neural network
  • image color analysis
  • Image filtering
  • image reconstruction

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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