Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors

We present a novel paradigm for ultra-low-bitrate imagecompression (ULB-IC) that exploits the “temporal” evolution in gener-ative image compression. Specifically, we define an explicit intermediatestate during decoding: a compact anchor frame, which preserves the scenegeometry and semantic layout while discarding high-frequency details.We then reinterpret generative decoding as a virtual temporal transitionfrom this anchor to the final reconstructed image. To model this progres-sion, we leverage a pretrained video diffusion model (VDM) as a temporalprior: the anchor frame serves as the initial frame and the original imageas the target frame, transforming the decoding process into a next-frameprediction task. In contrast to image diffusion-based ULB-IC models, ourdecoding proceeds from a visible, semantically faithful anchor, which im-proves both fidelity and realism for perceptual image compression. Ex-tensive experiments demonstrate that our method achieves superior rate-distortion performance. On the CLIC2020 test set, our method achievesover 50% bitrate savings across LPIPS, DISTS, FID, and KID com-pared to DiffC, while also delivering a significant decoding speedup of upto ×5. Code will be released at https://github.com/UnoC-727/NeFIC.