Autoencoder Based Image Compression: Can the Learning be Quantization Independent?
This paper explores the problem of learning transforms for image compression via autoencoders. Usually, the ratedistortion performances of image compression are tuned by varying the quantization step size. In the case of autoen coders, this in principle would require learning one transform per rate distortion point at a given quantization step size. Here, we show that comparable performances can be obtained with a unique learned transform. The different rate distortion points are then reached by varying the quantization step size at test time. This approach saves a lot of training time.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)