SPRDiff is a diffusion model for ultra-low bitrate image compression that fuses features from distortion-oriented, semantic-oriented, and VAE encoders plus a dual-feature reconstruction module to outperform prior methods on rate-distortion-perception trade-offs.
A residual diffusion model for high perceptual quality codec augmentation
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RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.
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Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
SPRDiff is a diffusion model for ultra-low bitrate image compression that fuses features from distortion-oriented, semantic-oriented, and VAE encoders plus a dual-feature reconstruction module to outperform prior methods on rate-distortion-perception trade-offs.
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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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A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.