  Face video super-resolution algorithm aims to reconstruct realistic face
details through continuous input video sequences. However, existing video
processing algorithms usually contain redundant parameters to guarantee
different super-resolution scenes. In this work, we focus on super-resolution
of face areas in original video scenes, while rest areas are interpolated. This
specific super-resolved task makes it possible to cut redundant parameters in
general video super-resolution networks. We construct a dataset consisting
entirely of face video sequences for network training and evaluation, and
conduct hyper-parameter optimization in our experiments. We use three combined
strategies to optimize the network parameters with a simultaneous
train-evaluation method to accelerate optimization process. Results show that
simultaneous train-evaluation method improves the training speed and
facilitates the generation of efficient networks. The generated network can
reduce at least 52.4% parameters and 20.7% FLOPs, achieve better performance on
PSNR, SSIM compared with state-of-art video super-resolution algorithms. When
processing 36x36x1x3 input video frame sequences, the efficient network
provides 47.62 FPS real-time processing performance. We name our proposal as
hyper-parameter optimization for face Video Super-Resolution (HO-FVSR), which
is open-sourced at https://github.com/yphone/efficient-network-for-face-VSR.
