  The advances in deep generative models have greatly accelerate the process of
video procession such as video enhancement and synthesis. Learning
spatio-temporal video models requires to capture the temporal dynamics of a
scene, in addition to the visual appearance of individual frames. Illumination
consistency, which reflects the variations of illumination in the dynamic video
sequences, play a vital role in video processing. Unfortunately, to date, no
well-accepted quantitative metric has been proposed for video illumination
consistency evaluation. In this paper, we propose a illumination histogram
consistency (IHC) metric to quantitatively and automatically evaluate the
illumination consistency of the video sequences. IHC measures the illumination
variation of any video sequence based on the illumination histogram
discrepancies across all the frames in the video sequence. Specifically, given
a video sequence, we first estimate the illumination map of each individual
frame using the Retinex model; Then, using the illumination maps, the mean
illumination histogram of the video sequence is computed by the mean operation
across all the frames; Next, we compute the illumination histogram discrepancy
between each individual frame and the mean illumination histogram and sum up
all the illumination histogram discrepancies to represent the illumination
variations of the video sequence. Finally, we obtain the IHC score from the
illumination histogram discrepancies via normalization and subtraction
operations. Experiments are conducted to illustrate the performance of the
proposed IHC metric and its capability to measure the illumination variations
in video sequences. The source code is available on
\url{https://github.com/LongChenCV/IHC-Metric}.
