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.
2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW)