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 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.
Original language | English |
---|---|
Title of host publication | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
Publisher | IEEE Computer Society |
Pages | 16092-16101 |
Number of pages | 10 |
ISBN (Electronic) | 9781665469463 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States Duration: 2022 Jun 19 → 2022 Jun 24 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
---|---|
Volume | 2022-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 |
---|---|
Country/Territory | United States |
City | New Orleans |
Period | 22/6/19 → 22/6/24 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Low-level vision
- Representation learning
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition