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Recently, learned image compression techniques have achieved remarkableperformance, even surpassing the best manually designed lossy image coders.They are promising to be large-scale adopted. For the sake of practicality, athorough investigation of the architecture design of learned image compression,regarding both compression performance and running speed, is essential. In thispaper, we first propose uneven channel-conditional adaptive coding, motivatedby the observation of energy compaction in learned image compression. Combiningthe proposed uneven grouping model with existing context models, we obtain aspatial-channel contextual adaptive model to improve the coding performancewithout damage to running speed. Then we study the structure of the maintransform and propose an efficient model, ELIC, to achieve state-of-the-artspeed and compression ability. With superior performance, the proposed modelalso supports extremely fast preview decoding and progressive decoding, whichmakes the coming application of learning-based image compression morepromising.

ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding