FaithEIR combines learnable reversible latent transformations, an adaptive high-frequency detail prior, and semantic conditioning to outperform prior methods in fidelity and perceptual quality for extreme image rescaling.
Oscar: One-step diffusion codec across multiple bit-rates.arXiv preprint arXiv:2505.16091
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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.
CoD-Lite delivers real-time generative image compression via a lightweight convolution-based diffusion codec with compression-oriented pre-training and distillation, achieving substantial bitrate savings.
RDVQ enables joint rate-distortion optimization for vector-quantized generative image compression via differentiable codebook distribution relaxation and an autoregressive entropy model.
citing papers explorer
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Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors
FaithEIR combines learnable reversible latent transformations, an adaptive high-frequency detail prior, and semantic conditioning to outperform prior methods in fidelity and perceptual quality for extreme image rescaling.
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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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CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
CoD-Lite delivers real-time generative image compression via a lightweight convolution-based diffusion codec with compression-oriented pre-training and distillation, achieving substantial bitrate savings.
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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.