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

DPICT: Deep Progressive Image Compression Using Trit-Planes