1 citations · 1 across the 4 of their papers we have counts for
6 papers
ResGVC: Residual Coding for High-fidelity Generative Video Compression
Ding Ding, Daowen Li, Ying Chen +1
Generative video compression (GVC) methods usually employ decoder-side diffusion models to denoise reconstructed signals based on generative priors. Besides, most of these methods…
Generative Video Compression Based on Hierarchical Referencing
Daowen Li, Ding Ding, Zifu Zhang +2
Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be encoded efficiently while ser…
Dense-Prior-Guided Generative Video Compression
Ding Ding, Daowen Li, Ying Chen +4
Diffusion-based generative video compression offers a promising paradigm for low-bitrate reconstruction, but existing keyframe-based controllable approaches rely on sparse priors f…
ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling
Daowen Li, Ruixiao Dong, Kai Li +3
Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native suppor…
Transferable Learned Image Compression-Resistant Adversarial Perturbations
Yang Sui, Zhuohang Li, Ding Ding +4
Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are pr…
Corner-to-Center Long-range Context Model for Efficient Learned Image Compression
Yang Sui, Ding Ding, Xiang Pan +4
In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time result…