  Learned video compression methods have gained a variety of interest in the
video coding community since they have matched or even exceeded the
rate-distortion (RD) performance of traditional video codecs. However, many
current learning-based methods are dedicated to utilizing short-range temporal
information, thus limiting their performance. In this paper, we focus on
exploiting the unique characteristics of video content and further exploring
temporal information to enhance compression performance. Specifically, for
long-range temporal information exploitation, we propose temporal prior that
can update continuously within the group of pictures (GOP) during inference. In
that case temporal prior contains valuable temporal information of all decoded
images within the current GOP. As for short-range temporal information, we
propose a progressive guided motion compensation to achieve robust and
effective compensation. In detail, we design a hierarchical structure to
achieve multi-scale compensation. More importantly, we use optical flow
guidance to generate pixel offsets between feature maps at each scale, and the
compensation results at each scale will be used to guide the following scale's
compensation. Sufficient experimental results demonstrate that our method can
obtain better RD performance than state-of-the-art video compression
approaches. The code is publicly available on:
https://github.com/Huairui/LSTVC.

Image and Video Processing;Computer Vision and Pattern Recognition