  Neural image compression has surpassed state-of-the-art traditional codecs
(H.266/VVC) for rate-distortion (RD) performance, but suffers from large
complexity and separate models for different rate-distortion trade-offs. In
this paper, we propose an Efficient single-model Variable-bit-rate Codec (EVC),
which is able to run at 30 FPS with 768x512 input images and still outperforms
VVC for the RD performance. By further reducing both encoder and decoder
complexities, our small model even achieves 30 FPS with 1920x1080 input images.
To bridge the performance gap between our different capacities models, we
meticulously design the mask decay, which transforms the large model's
parameters into the small model automatically. And a novel sparsity
regularization loss is proposed to mitigate shortcomings of $L_p$
regularization. Our algorithm significantly narrows the performance gap by 50%
and 30% for our medium and small models, respectively. At last, we advocate the
scalable encoder for neural image compression. The encoding complexity is
dynamic to meet different latency requirements. We propose decaying the large
encoder multiple times to reduce the residual representation progressively.
Both mask decay and residual representation learning greatly improve the RD
performance of our scalable encoder. Our code is at
https://github.com/microsoft/DCVC.
