6 papers
CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
Zhaoyang Jia, Naifu Xue, Zihan Zheng +7
Recent advanced diffusion methods typically derive strong generative priors by scaling diffusion transformers. However, scaling fails to generalize when adapted for real-time compr…
Generative Video Compression with One-Dimensional Latent Representation
Zihan Zheng, Zhaoyang Jia, Naifu Xue +7
Recent advancements in generative video codec (GVC) typically encode video into a 2D latent grid and employ high-capacity generative decoders for reconstruction. However, this para…
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
Naifu Xue, Zhaoyang Jia, Jiahao Li +4
While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates remains challenging. Some NVCs i…
CoD: A Diffusion Foundation Model for Image Compression
Zhaoyang Jia, Zihan Zheng, Naifu Xue +6
Existing diffusion codecs typically build on text-to-image diffusion foundation models like Stable Diffusion. However, text conditioning is suboptimal from a compression perspectiv…
One-Step Diffusion-Based Image Compression with Semantic Distillation
Naifu Xue, Zhaoyang Jia, Jiahao Li +3
While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the…
DLF: Extreme Image Compression with Dual-generative Latent Fusion
Naifu Xue, Zhaoyang Jia, Jiahao Li +3
Recent studies in extreme image compression have achieved remarkable performance by compressing the tokens from generative tokenizers. However, these methods often prioritize clust…