Learning Sparse auto-Encoders for Green AI image coding

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 ℓ
1
constraint, the ℓ
1,∞
and the new ℓ
1,1
constraint. Experimental results show that the ℓ
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.