Channel aggregation by feed-forward networks, not spatial attention, drives rate-distortion performance in transformer-based learned image compression, and simplified models achieve state-of-the-art results with over 30% faster decoding.
Two-stage octave residual network for end-to-end image compres- sion
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S2CFormer: Revisiting the RD-Latency Trade-off in Transformer-based Learned Image Compression
Channel aggregation by feed-forward networks, not spatial attention, drives rate-distortion performance in transformer-based learned image compression, and simplified models achieve state-of-the-art results with over 30% faster decoding.