  In this paper, we propose a new deep image compression framework called
Complexity and Bitrate Adaptive Network (CBANet), which aims to learn one
single network to support variable bitrate coding under different computational
complexity constraints. In contrast to the existing state-of-the-art learning
based image compression frameworks that only consider the rate-distortion
trade-off without introducing any constraint related to the computational
complexity, our CBANet considers the trade-off between the rate and distortion
under dynamic computational complexity constraints. Specifically, to decode the
images with one single decoder under various computational complexity
constraints, we propose a new multi-branch complexity adaptive module, in which
each branch only takes a small portion of the computational budget of the
decoder. The reconstructed images with different visual qualities can be
readily generated by using different numbers of branches. Furthermore, to
achieve variable bitrate decoding with one single decoder, we propose a bitrate
adaptive module to project the representation from a base bitrate to the
expected representation at a target bitrate for transmission. Then it will
project the transmitted representation at the target bitrate back to that at
the base bitrate for the decoding process. The proposed bit adaptive module can
significantly reduce the storage requirement for deployment platforms. As a
result, our CBANet enables one single codec to support multiple bitrate
decoding under various computational complexity constraints. Comprehensive
experiments on two benchmark datasets demonstrate the effectiveness of our
CBANet for deep image compression.
