This paper addresses the problem of image compression using
sparse representations. We propose a variant of autoencoder
called Stochastic Winner-Take-All Auto-Encoder
(SWTA AE). “Winner-Take-All” means that image patches
compete with one another when computing their sparse representation
and “Stochastic” indicates that a stochastic hyperparameter
rules this competition during training. Unlike
auto-encoders, SWTA AE performs variable rate image compression
for images of any size after a single training, which
is fundamental for compression. For comparison, we also
propose a variant of Orthogonal Matching Pursuit (OMP)
called Winner-Take-All Orthogonal Matching Pursuit (WTA
OMP). In terms of rate-distortion trade-off, SWTA AE outperforms
auto-encoders but it is worse than WTA OMP. Besides,
SWTA AE can compete with JPEG in terms of ratedistortion.