DPICT: Deep Progressive Image Compression Using Trit-Planes

• URL: http://arxiv.org/abs/2112.06334v1
• Date: Sun, 12 Dec 2021 22:09:33 GMT
• Title: DPICT: Deep Progressive Image Compression Using Trit-Planes
• Authors: Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, and Chang-Su Kim
• Abstract summary: Deep progressive image compression using trit-planes (DPICT) algorithm. We transform an image into a latent tensor using an analysis network. We encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance.
• Score: 36.34865777731784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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