TY - GEN
T1 - A Gamut-Extension Method Considering Color Information Restoration using Convolutional Neural Networks
AU - Takeuchi, Masaru
AU - Sakamoto, Yusuke
AU - Yokoyama, Ryota
AU - Sun, Heming
AU - Matsuo, Yasutaka
AU - Katto, Jiro
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Recently, Ultra HDTV (UHDTV) services become popular over satellite and on the internet. On the contrary, there are tremendously huge volume of High Definition Television (HDTV) and Standard Definition Television (SDTV) contents stored in broadcasting companies and storage devices. In this paper, we propose a color space conversion (also known as gamut mapping) method from BT. 709 (used for current HDTV broadcast) to BT. 2020 (used for UHDTV broadcast), which estimates and restores lost color information. It learns an end-to-end conversion method from BT. 709 image to BT. 2020 image with restoring lost color information using Convolutional Neural Network (CNN). By experiments, we confirm that our method can achieve 2.31dB gain against the conventional method on average.
AB - Recently, Ultra HDTV (UHDTV) services become popular over satellite and on the internet. On the contrary, there are tremendously huge volume of High Definition Television (HDTV) and Standard Definition Television (SDTV) contents stored in broadcasting companies and storage devices. In this paper, we propose a color space conversion (also known as gamut mapping) method from BT. 709 (used for current HDTV broadcast) to BT. 2020 (used for UHDTV broadcast), which estimates and restores lost color information. It learns an end-to-end conversion method from BT. 709 image to BT. 2020 image with restoring lost color information using Convolutional Neural Network (CNN). By experiments, we confirm that our method can achieve 2.31dB gain against the conventional method on average.
KW - color gamut
KW - convolutional neural network
KW - gamut extension
KW - image color analysis
KW - image reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85076800366&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076800366&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2019.8803728
DO - 10.1109/ICIP.2019.8803728
M3 - Conference contribution
AN - SCOPUS:85076800366
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 774
EP - 778
BT - 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PB - IEEE Computer Society
T2 - 26th IEEE International Conference on Image Processing, ICIP 2019
Y2 - 22 September 2019 through 25 September 2019
ER -