  Traditional video compression technologies have been developed over decades
in pursuit of higher coding efficiency. Efficient temporal information
representation plays a key role in video coding. Thus, in this paper, we
propose to exploit the temporal correlation using both first-order optical flow
and second-order flow prediction. We suggest an one-stage learning approach to
encapsulate flow as quantized features from consecutive frames which is then
entropy coded with adaptive contexts conditioned on joint spatial-temporal
priors to exploit second-order correlations. Joint priors are embedded in
autoregressive spatial neighbors, co-located hyper elements and temporal
neighbors using ConvLSTM recurrently. We evaluate our approach for the
low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC
and another learned video compression method, following the common test
settings. Our work offers the state-of-the-art performance, with consistent
gains across all popular test sequences.
