  Compression has been an important research topic for many decades, to produce
a significant impact on data transmission and storage. Recent advances have
shown a great potential of learning image and video compression. Inspired from
related works, in this paper, we present an image compression architecture
using a convolutional autoencoder, and then generalize image compression to
video compression, by adding an interpolation loop into both encoder and
decoder sides. Our basic idea is to realize spatial-temporal energy compaction
in learning image and video compression. Thereby, we propose to add a spatial
energy compaction-based penalty into loss function, to achieve higher image
compression performance. Furthermore, based on temporal energy distribution, we
propose to select the number of frames in one interpolation loop, adapting to
the motion characteristics of video contents. Experimental results demonstrate
that our proposed image compression outperforms the latest image compression
standard with MS-SSIM quality metric, and provides higher performance compared
with state-of-the-art learning compression methods at high bit rates, which
benefits from our spatial energy compaction approach. Meanwhile, our proposed
video compression approach with temporal energy compaction can significantly
outperform MPEG-4 and is competitive with commonly used H.264. Both our image
and video compression can produce more visually pleasant results than
traditional standards.
