  Recent deep-learning-based video compression methods brought coding gains
over conventional codecs such as AVC and HEVC. However, learning-based codecs
generally require considerable computation time and model complexity. In this
paper, we propose a new lightweight hybrid video codec consisting of a
conventional video codec(HEVC / VVC), a lossless image codec, and our new
restoration network. Precisely, our encoder consists of the conventional video
encoder and a lossless image encoder, transmitting a lossy-compressed video
bitstream along with a losslessly-compressed reference frame. The decoder is
constructed with corresponding video/image decoders and a new restoration
network, which enhances the compressed video in two-step processes. In the
first step, a network trained with a large video dataset restores the details
lost by the conventional encoder. Then, we further boost the video quality with
the guidance of a reference image, which is a losslessly compressed video
frame. The reference image provides video-specific information, which can be
utilized to better restore the details of a compressed video. Experimental
results show that the proposed method achieves comparable performance to
top-tier methods, even when applied to HEVC. Nevertheless, our method has lower
complexity, a faster run time, and can be easily integrated into existing
conventional codecs.
