  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.
