Shallow sparse autoencoders versus sparse coding algorithms for image compression
This paper considers the problem of image compression with shallow sparse autoencoders. We use both a T-sparse autoencoder (T-sparse AE) and a winner-take-all autoencoder (WTA AE). A performance analysis in terms of rate-distortion trade-off and complexity is conducted, comparing with LARS-Lasso, Coordinate Descent (CoD) and Orthogonal Matching Pursuit (OMP). We show that, WTA AE achieves the best rate-distortion trade-off, it is robust to quantization noise and it is less complex than LARS-Lasso, CoD and OMP.