Skip to main content

Learning sparse auto-encoders for green AI image coding

Cyprien Gille (UMONS); Frederic Guyard (Orange Labs); Marc Antonini (Universite Nice Sophia Antipolis); Michel Barlaud (University of Nice)

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
06 Jun 2023

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 this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical $\ell_1$ constraint, the $\ell_{1,\infty}$ and the new $\ell_{1,1}$ constraint. Experimental results show that the $\ell_{1,1}$ constraint provides the best structured sparsity, resulting in a high reduction of memory ( 82 \%) and computational cost reduction (25 \%), with similar rate-distortion performance as with dense networks.

More Like This

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00