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)
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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.