BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering
<|reference_start|>BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video Deflickering: Developing blind video deflickering (BVD) algorithms to enhance video temporal consistency, is gaining importance amid the flourish of image processing and video generation. However, the intricate nature of video data complicates the training of deep learning methods, leading to high resource consumption and instability, notably under severe lighting flicker. This underscores the critical need for a compact representation beyond pixel values to advance BVD research and applications. Inspired by the classic scale-time equalization (STE), our work introduces the histogram-assisted solution, called BlazeBVD, for high-fidelity and rapid BVD. Compared with STE, which directly corrects pixel values by temporally smoothing color histograms, BlazeBVD leverages smoothed illumination histograms within STE filtering to ease the challenge of learning temporal data using neural networks. In technique, BlazeBVD begins by condensing pixel values into illumination histograms that precisely capture flickering and local exposure variations. These histograms are then smoothed to produce singular frames set, filtered illumination maps, and exposure maps. Resorting to these deflickering priors, BlazeBVD utilizes a 2D network to restore faithful and consistent texture impacted by lighting changes or localized exposure issues. BlazeBVD also incorporates a lightweight 3D network to amend slight temporal inconsistencies, avoiding the resource consumption issue. Comprehensive experiments on synthetic, real-world and generated videos, showcase the superior qualitative and quantitative results of BlazeBVD, achieving inference speeds up to 10x faster than state-of-the-arts.<|reference_end|>
@article{qiu2024blazebvd:,
    title={BlazeBVD: Make Scale-Time Equalization Great Again for Blind Video
  Deflickering},
    author={Xinmin Qiu, Congying Han, Zicheng Zhang, Bonan Li, Tiande Guo, Pingyu
  Wang, Xuecheng Nie},
    journal={arXiv preprint arXiv:2403.06243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06243},
    primaryClass={cs.CV}
}