ProGIC applies residual vector quantization with a lightweight CNN-attention backbone to deliver progressive generative image compression with claimed perceptual gains and over 10x faster encoding/decoding versus MS-ILLM.
Mlic++: Linear complexity multi-reference en- tropy modeling for learned image compression.ACM Trans
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ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization
ProGIC applies residual vector quantization with a lightweight CNN-attention backbone to deliver progressive generative image compression with claimed perceptual gains and over 10x faster encoding/decoding versus MS-ILLM.