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CONTENT-AWARE SUPERVISION FOR DIFFUSION-BASED RESTORATION OF EXTREMELY COMPRESSED BACKGROUND FOR VCM

Authors
Dao, Le Thi HueVien, An GiaLee, JooyoungJeong, SeyoonYang, NaeunLee, Chul
Issue Date
2024
Publisher
IEEE
Keywords
diffusion model; Image generation; image restoration; video coding for machines (VCM)
Citation
2024 IEEE International Conference on Image Processing (ICIP), pp 1683 - 1689
Pages
7
Indexed
SCOPUS
Journal Title
2024 IEEE International Conference on Image Processing (ICIP)
Start Page
1683
End Page
1689
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/57899
DOI
10.1109/ICIP51287.2024.10648159
ISSN
1522-4880
2381-8549
Abstract
We propose content-aware supervision (CAS) techniques for diffusion-based restoration of an extremely compressed background for video coding for machines (VCM). First, we develop a CAS block to exploit prior information in an input image to reconstruct the noisy image, which is used as the input for the pretrained diffusion model. Then, we construct a refinement block to guide the pretrained diffusion model at each diffusion step by incorporating a degradation model and correction gradient estimation. Experimental results demonstrate the proposed algorithm outperforms state-of-the-art algorithms. © 2024 IEEE.
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