  Neural image compression leverages deep neural networks to outperform
traditional image codecs in rate-distortion performance. However, the resulting
models are also heavy, computationally demanding and generally optimized for a
single rate, limiting their practical use. Focusing on practical image
compression, we propose slimmable compressive autoencoders (SlimCAEs), where
rate (R) and distortion (D) are jointly optimized for different capacities.
Once trained, encoders and decoders can be executed at different capacities,
leading to different rates and complexities. We show that a successful
implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs.
Our experiments show that SlimCAEs are highly flexible models that provide
excellent rate-distortion performance, variable rate, and dynamic adjustment of
memory, computational cost and latency, thus addressing the main requirements
of practical image compression.
