  Conventional video compression methods employ a linear transform and block
motion model, and the steps of motion estimation, mode and quantization
parameter selection, and entropy coding are optimized individually due to
combinatorial nature of the end-to-end optimization problem. Learned video
compression allows end-to-end rate-distortion optimized training of all
nonlinear modules, quantization parameter and entropy model simultaneously.
While previous work on learned video compression considered training a
sequential video codec based on end-to-end optimization of cost averaged over
pairs of successive frames, it is well-known in conventional video compression
that hierarchical, bi-directional coding outperforms sequential compression. In
this paper, we propose for the first time end-to-end optimization of a
hierarchical, bi-directional motion compensated learned codec by accumulating
cost function over fixed-size groups of pictures (GOP). Experimental results
show that the rate-distortion performance of our proposed learned
bi-directional {\it GOP coder} outperforms the state-of-the-art end-to-end
optimized learned sequential compression as expected.
