  In this paper, we propose a novel variable-rate learned image compression
framework with a conditional autoencoder. Previous learning-based image
compression methods mostly require training separate networks for different
compression rates so they can yield compressed images of varying quality. In
contrast, we train and deploy only one variable-rate image compression network
implemented with a conditional autoencoder. We provide two rate control
parameters, i.e., the Lagrange multiplier and the quantization bin size, which
are given as conditioning variables to the network. Coarse rate adaptation to a
target is performed by changing the Lagrange multiplier, while the rate can be
further fine-tuned by adjusting the bin size used in quantizing the encoded
representation. Our experimental results show that the proposed scheme provides
a better rate-distortion trade-off than the traditional variable-rate image
compression codecs such as JPEG2000 and BPG. Our model also shows comparable
and sometimes better performance than the state-of-the-art learned image
compression models that deploy multiple networks trained for varying rates.
