Geodesy

Computer hardware

Algorithm

Image compression

Traverse

Materials science

Theoretical computer science

Image processing

Computer science

Bitstream

Codec

Decoding methods

Geography

Image (mathematics)

Artificial intelligence

Composite material

Data compression

Compression (physics)

Computer vision

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 tritplanes, 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.

DPICT: Deep Progressive Image Compression Using Trit-Planes