Autoencoder based image compression: can the learning be quantization independent?

Notably, the discrete cosine transform (DCT) is the most commonly used for two reasons: (i) it is image-independent, implying that the DCT does not need to be transmitted, (ii) it approaches the optimal orthogonal transform in terms of rate-distortion, assuming that natural images can be modeled by zero-mean Gaussian-Markov processes with high correlation [1]. Deep autoencoders have been shown as promising tools for finding alternative transforms [2, 3, 4]. Autoencoders learn the encoder-decoder nonlinear transform from natural images. In the best image compression algorithms based on autoencoders [5, 6, 7], one transform is learned per ratedistortion point at a given quantization step size. Then, the quantization step size remains unchanged at test time so that the training and test conditions are identical. By contrast, image coding standards implement adaptive quantizations [8, 9]. Should the quantization be imposed during the training? To answer this, we propose an approach where the transform and the quantization are learned jointly.