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Neural image compression has surpassed state-of-the-art traditional codecs(H.266/VVC) for rate-distortion (RD) performance, but suffers from largecomplexity and separate models for different rate-distortion trade-offs. Inthis 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 outperformsVVC for the RD performance. By further reducing both encoder and decodercomplexities, our small model even achieves 30 FPS with 1920x1080 input images.To bridge the performance gap between our different capacities models, wemeticulously design the mask decay, which transforms the large model'sparameters into the small model automatically. And a novel sparsityregularization 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 thescalable encoder for neural image compression. The encoding complexity isdynamic to meet different latency requirements. We propose decaying the largeencoder multiple times to reduce the residual representation progressively.Both mask decay and residual representation learning greatly improve the RDperformance of our scalable encoder. Our code is athttps://github.com/microsoft/DCVC.

EVC: Towards Real-Time Neural Image Compression with Mask Decay