5 papers
Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution
Jinpei Guo, Yifei Ji, Shengwei Wang +8
Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, sin…
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression
Zheng Chen, Mingde Zhou, Jinpei Guo +3
Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-s…
OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Jinpei Guo, Yifei Ji, Zheng Chen +6
Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconst…
SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal
Tingyu Yang, Jue Gong, Jinpei Guo +3
JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoratio…
Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
Jinpei Guo, Zheng Chen, Wenbo Li +2
Diffusion models have demonstrated remarkable success in image restoration tasks. However, their multi-step denoising process introduces significant computational overhead, limitin…