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.
Neural discrete representation learning.Advances in neural information pro- cessing systems, 30, 2017
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.