  Deep learning, e.g., convolutional neural networks (CNNs), has achieved great
success in image processing and computer vision especially in high level vision
applications such as recognition and understanding. However, it is rarely used
to solve low-level vision problems such as image compression studied in this
paper. Here, we move forward a step and propose a novel compression framework
based on CNNs. To achieve high-quality image compression at low bit rates, two
CNNs are seamlessly integrated into an end-to-end compression framework. The
first CNN, named compact convolutional neural network (ComCNN), learns an
optimal compact representation from an input image, which preserves the
structural information and is then encoded using an image codec (e.g., JPEG,
JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural
network (RecCNN), is used to reconstruct the decoded image with high-quality in
the decoding end. To make two CNNs effectively collaborate, we develop a
unified end-to-end learning algorithm to simultaneously learn ComCNN and
RecCNN, which facilitates the accurate reconstruction of the decoded image
using RecCNN. Such a design also makes the proposed compression framework
compatible with existing image coding standards. Experimental results validate
that the proposed compression framework greatly outperforms several compression
frameworks that use existing image coding standards with state-of-the-art
deblocking or denoising post-processing methods.
