Abstract
With the development of deep learning technology, the automated grayscale image colorization methods have been widely used. As color space is very important in colorization, we analyzed 3 different color spaces, named YUV, Lab, and HSV. We used deep learning methods to evaluate the effect of different color spaces to image colorization methods. The colorization method was based on the VGG16 and the Residual Encoder model. The results demonstrated that the quality of color transfer in the YUV and Lab color spaces is better than that in the HSV color space.
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Acknowledgements
This work was supported by Fundamental Research Funds for National Key Research and Development Program of China, Grant/Award Numbers: SQ2020YFC150101, 2017YFB0504202, 2018YFB10046; National Natural Science Foundation of China, Grant/Award Number: 41671441.
Ethical Approval The experiments were carried out in accordance with the ethical guidelines and the images used are selected from ImageNet dataset.
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Yu, W., Cao, L., Li, Z., Xia, S. (2021). Analysis of the Influence of Color Space Selection on Color Transfer. In: Zhao, P., Ye, Z., Xu, M., Yang, L., Zhang, L., Zhu, R. (eds) Advances in Graphic Communication, Printing and Packaging Technology and Materials. Lecture Notes in Electrical Engineering, vol 754. Springer, Singapore. https://doi.org/10.1007/978-981-16-0503-1_25
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DOI: https://doi.org/10.1007/978-981-16-0503-1_25
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