Video Colorization:A Survey  

作　　者：彭中正 杨艺新 唐金辉 潘金山 Zhong-Zheng Peng;Yi-Xin Yang;Jin-Hui Tang;Jin-Shan Pan(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;CCF;IEEE;ACM)

机构地区：[1]School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,210094,China [2]CCF [3]IEEE [4]ACM

出　　处：《Journal of Computer Science & Technology》2024年第3期487-508,共22页计算机科学技术学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.U22B2049 and 62332010.

摘　　要：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.

关 键 词：video colorization deep convolutional neural network spatial-temporal consistency 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]