  Recent years have witnessed an increasing interest in end-to-end learned
video compression. Most previous works explore temporal redundancy by detecting
and compressing a motion map to warp the reference frame towards the target
frame. Yet, it failed to adequately take advantage of the historical priors in
the sequential reference frames. In this paper, we propose an Advanced Learned
Video Compression (ALVC) approach with the in-loop frame prediction module,
which is able to effectively predict the target frame from the previously
compressed frames, without consuming any bit-rate. The predicted frame can
serve as a better reference than the previously compressed frame, and therefore
it benefits the compression performance. The proposed in-loop prediction module
is a part of the end-to-end video compression and is jointly optimized in the
whole framework. We propose the recurrent and the bi-directional in-loop
prediction modules for compressing P-frames and B-frames, respectively. The
experiments show the state-of-the-art performance of our ALVC approach in
learned video compression. We also outperform the default hierarchical B mode
of x265 in terms of PSNR and beat the slowest mode of the SSIM-tuned x265 on
MS-SSIM. The project page: https://github.com/RenYang-home/ALVC.
