RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
Lossy image compression with conditional diffusion models.Advances in Neural In- formation Processing Systems, 36:64971–64995
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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.
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Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
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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.