  We propose the deep progressive image compression using trit-planes (DPICT)
algorithm, which is the first learning-based codec supporting fine granular
scalability (FGS). First, we transform an image into a latent tensor using an
analysis network. Then, we represent the latent tensor in ternary digits
(trits) and encode it into a compressed bitstream trit-plane by trit-plane in
the decreasing order of significance. Moreover, within each trit-plane, we sort
the trits according to their rate-distortion priorities and transmit more
important information first. Since the compression network is less optimized
for the cases of using fewer trit-planes, we develop a postprocessing network
for refining reconstructed images at low rates. Experimental results show that
DPICT outperforms conventional progressive codecs significantly, while enabling
FGS transmission. Codes are available at
https://github.com/jaehanlee-mcl/DPICT.
