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 strug-gle to meet specific colorization requirements.We also discuss the existing challenges and highlight several future research opportunities worth exploring.
video colorizationdeep convolutional neural networkspatial-temporal consistency
彭中正、杨艺新、唐金辉、潘金山
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School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
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维普
