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彭中正, 杨艺新, 唐金辉, 潘金山. 视频着色综述[J]. 计算机科学技术学报, 2024, 39(3): 487-508. DOI: 10.1007/s11390-024-4143-z
引用本文: 彭中正, 杨艺新, 唐金辉, 潘金山. 视频着色综述[J]. 计算机科学技术学报, 2024, 39(3): 487-508. DOI: 10.1007/s11390-024-4143-z
Peng ZZ, Yang YX, Tang JH et al. Video colorization: A survey. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(3): 487−508 May 2024. DOI: 10.1007/s11390-024-4143-z.
Citation: Peng ZZ, Yang YX, Tang JH et al. Video colorization: A survey. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(3): 487−508 May 2024. DOI: 10.1007/s11390-024-4143-z.

视频着色综述

Video Colorization: A Survey

  • 摘要: 受限于早期成像技术的发展水平,大量历史悠久且含有深刻人文情感的视频资料是以黑白视频的形式留存,未能体现真实色彩,并且随时间流逝重新复现原拍摄场景变得不可能。随着科技的不断进步,公众越来越追求高品质的观影体验,以至于传统的单色影片在观影体验上的表现稍显逊色,进而使得对视频彩色化技术需求的不断增长。视频着色是一项经典的计算机视觉任务,其目的是将黑白视频转换为彩色视频。虽然现有方法在图像着色领域已经取得了显著的进展,但视频着色任务更具挑战性。因为视频着色不仅需要关注每帧的着色效果,而且必须处理帧与帧之间的时序一致性。结果表明,将基于图像的着色方法直接应用于视频着色并不能得到令人满意的结果,例如当前最新图像方法DDColor (PSNR=30.54, FID=85.41和CDC=0.004168)。此外,由于全自动视频着色方法更注重时间一致性,而忽略了每个单独帧的着色性能。因此,全自动方法(FAVC)在测试数据集上的FID值(116.64)相对较高,着色性能相对较差。与上述策略相比,基于样例的着色方法(BiSTNet)已经证明能够产生更好的视频着色结果(PSNR=33.72, FID=45.18和0.004168)。最后,我们分析了各种视频着色方法存在的问题。比如,基于光流的方法很大程度依赖于光流估计的精度,基于涂鸦的方法需要大量的用户交互和修改,基于样例的方法较难获得好的参考帧,而基于全自动的方法很难保证视频的着色质量。通过对以上这些方法的总结和概述,期望能够进一步促进未来视频着色的研究工作,并为探索新的研究路径提供方向。

     

    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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