Learning sparse auto-encoders for green AI image coding. (arXiv:2209.04448v1 [eess.IV])

Recently, convolutional auto-encoders (CAE) were introduced for image coding.
They achieved performance improvements over the state-of-the-art JPEG2000
method. However, these performances were obtained using massive CAEs featuring
a large number of parameters and whose training required heavy computational
power.\\ In this paper, we address the problem of lossy image compression using
a CAE with a small memory footprint and low computational power usage. In order
to overcome the computational cost issue, the majority of the literature uses
Lagrangian proximal regularization …

arxiv coding green ai image