  Recently deep learning-based image compression has shown the potential to
outperform traditional codecs. However, most existing methods train multiple
networks for multiple bit rates, which increase the implementation complexity.
In this paper, we propose a new variable-rate image compression framework,
which employs generalized octave convolutions (GoConv) and generalized octave
transposed-convolutions (GoTConv) with built-in generalized divisive
normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and
GoTConv-based residual blocks are also developed in the encoder and decoder
networks. Our scheme also uses a stochastic rounding-based scalar quantization.
To further improve the performance, we encode the residual between the input
and the reconstructed image from the decoder network as an enhancement layer.
To enable a single model to operate with different bit rates and to learn
multi-rate image features, a new objective function is introduced. Experimental
results show that the proposed framework trained with variable-rate objective
function outperforms the standard codecs such as H.265/HEVC-based BPG and
state-of-the-art learning-based variable-rate methods.
