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Video Colorization: A Survey

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Abstract

Video colorization aims to add color to grayscale or monochrome videos. Although existing methods have achieved substantial and noteworthy results in the field of image colorization, video colorization presents more formidable obstacles due to the additional necessity for temporal consistency. Moreover, there is rarely a systematic review of video colorization methods. In this paper, we aim to review existing state-of-the-art video colorization methods. In addition, maintaining spatial-temporal consistency is pivotal to the process of video colorization. To gain deeper insight into the evolution of existing methods in terms of spatial-temporal consistency, we further review video colorization methods from a novel perspective. Video colorization methods can be categorized into four main categories: optical-flow based methods, scribble-based methods, exemplar-based methods, and fully automatic methods. However, optical-flow based methods rely heavily on accurate optical-flow estimation, scribble-based methods require extensive user interaction and modifications, exemplar-based methods face challenges in obtaining suitable reference images, and fully automatic methods often struggle to meet specific colorization requirements. We also discuss the existing challenges and highlight several future research opportunities worth exploring.

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  1. Co-First Author (Zhong-Zheng Peng wrote the section about the classification of video colorization methods, and Yi-Xin Yang wrote the introduction. Zhong-Zheng Peng and Yi-Xin Yang jointly participated in the remaining sections.)

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhong-Zheng Peng  (彭中正), Yi-Xin Yang  (杨艺新), Jin-Hui Tang  (唐金辉) & Jin-Shan Pan  (潘金山)

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  1. Zhong-Zheng Peng  (彭中正)
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Correspondence to Jin-Shan Pan  (潘金山).

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Additional information

This work was supported by the National Natural Science Foundation of China under Grant Nos. U22B2049 and 62332010.

Zhong-Zheng Peng is currently pursuing his Ph.D. degree in School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing. His research interests include image/video colorization, super-resolution, and other restoration tasks.

Yi-Xin Yang is currently pursuing his Ph.D. degree in School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing. He received his M.S. degree in electrical engineering from University of California, Riverside, and B.S. degree in electronic information engineering from University of Electronic Science and Technology of China, Chengdu. His research interests include image/video colorization, super-resolution, and other restoration tasks.

Jin-Hui Tang received his B.E. and Ph.D. degrees from the University of Science and Technology of China, Hefei, in 2003 and 2008, respectively. He is currently a professor with the Nanjing University of Science and Technology, Nanjing. His research interests include multimedia analysis and computer vision.

Jin-Shan Pan received his Ph.D. degree in computational mathematics from the Dalian University of Technology, Dalian, in 2017. He is a professor of School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing. His research interest includes image deblurring, image/video analysis and enhancement, and related vision problems.

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Peng, ZZ., Yang, YX., Tang, JH. et al. Video Colorization: A Survey. J. Comput. Sci. Technol. 39, 487–508 (2024). https://doi.org/10.1007/s11390-024-4143-z

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