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Cool-Chic: Perceptually Tuned Low Complexity Overfitted Image Coder

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 19 Mar 2024Publisher:IEEEJournal:2024 Data Compression Conference (DCC)
Authors: Ladune, Théo; Philippe, Pierrick; Clare, Gordon; Henry, Félix; Leguay, Thomas;

doi: 10.1109/dcc58796.2024.00082

arXiv: 2401.02156

Cool-Chic: Perceptually Tuned Low Complexity Overfitted Image Coder

Abstract

This paper summarises the design of the Cool-Chic candidate for the Challenge on Learned Image Compression. This candidate attempts to demonstrate that neural coding methods can lead to low complexity and lightweight image decoders while still offering competitive performance. The approach is based on the already published overfitted lightweight neural networks Cool-Chic, further adapted to the human subjective viewing targeted in this challenge.

Comment: Challenge on Learned Image Compression (CLIC), DCC2024

Keywords

Electrical Engineering and Systems Science - Image and Video Processing

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green