  In this paper, we propose an image re-sampling compression method by learning
virtual codec network (VCN) to resolve the non-differentiable problem of
quantization function for image compression. Here, the image re-sampling not
only refers to image full-resolution re-sampling but also low-resolution
re-sampling. We generalize this method for standard-compliant image compression
(SCIC) framework and deep neural networks based compression (DNNC) framework.
Specifically, an input image is measured by re-sampling network (RSN) network
to get re-sampled vectors. Then, these vectors are directly quantized in the
feature space in SCIC, or discrete cosine transform coefficients of these
vectors are quantized to further improve coding efficiency in DNNC. At the
encoder, the quantized vectors or coefficients are losslessly compressed by
arithmetic coding. At the receiver, the decoded vectors are utilized to restore
input image by image decoder network (IDN). In order to train RSN network and
IDN network together in an end-to-end fashion, our VCN network intimates
projection from the re-sampled vectors to the IDN-decoded image. As a result,
gradients from IDN network to RSN network can be approximated by VCN network's
gradient. Because dimension reduction can be further achieved by quantization
in some dimensional space after image re-sampling within auto-encoder
architecture, we can well initialize our networks from pre-trained auto-encoder
networks. Through extensive experiments and analysis, it is verified that the
proposed method has more effectiveness and versatility than many
state-of-the-art approaches.
