  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.
