  This paper presents a novel convolutional neural network (CNN) based image
compression framework via scalable auto-encoder (SAE). Specifically, our SAE
based deep image codec consists of hierarchical coding layers, each of which is
an end-to-end optimized auto-encoder. The coarse image content and texture are
encoded through the first (base) layer while the consecutive (enhance) layers
iteratively code the pixel-level reconstruction errors between the original and
former reconstructed images. The proposed SAE structure alleviates the need to
train multiple models for different bit-rate points by recently proposed
auto-encoder based codecs. The SAE layers can be combined to realize multiple
rate points, or to produce a scalable stream. The proposed method has similar
rate-distortion performance in the low-to-medium rate range as the
state-of-the-art CNN based image codec (which uses different optimized networks
to realize different bit rates) over a standard public image dataset.
Furthermore, the proposed codec generates better perceptual quality in this bit
rate range.
