Improving Video Colorization by Test-Time Tuning
Yaping Zhao Haitian Zheng Jiebo Luo Edmund Y. Lam
With the advancements in deep learning, video colorization by propagating
color information from a colorized reference frame to a monochrome video
sequence has been well explored. However, the existing approaches often suffer
from overfitting the training dataset and sequentially lead to suboptimal
performance on colorizing testing samples. To address this issue, we propose an
effective method, which aims to enhance video colorization through test-time
tuning. By exploiting the reference to construct additional training samples
during testing, our approach achieves a performance boost of 1~3 dB in PSNR on
average compared to the baseline. Code is available at:
https://github.com/IndigoPurple/T3