  End-to-end optimized neural image compression (NIC) has obtained superior
lossy compression performance recently. In this paper, we consider the problem
of rate-distortion (R-D) characteristic analysis and modeling for NIC. We make
efforts to formulate the essential mathematical functions to describe the R-D
behavior of NIC using deep networks. Thus arbitrary bit-rate points could be
elegantly realized by leveraging such model via a single trained network. We
propose a plugin-in module to learn the relationship between the target
bit-rate and the binary representation for the latent variable of auto-encoder.
The proposed scheme resolves the problem of training distinct models to reach
different points in the R-D space. Furthermore, we model the rate and
distortion characteristic of NIC as a function of the coding parameter
$\lambda$ respectively. Our experiments show our proposed method is easy to
adopt and realizes state-of-the-art continuous bit-rate coding performance,
which implies that our approach would benefit the practical deployment of NIC.
