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Ding Ding

6 papers hereh-index 349 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • eess.IV1
same name
  • Ding Ding — 29 papers, h 5
  • Ding Ding — 5 papers, h 4
  • Ding Ding — 5 papers, h 3
  • Ding Ding — 3 papers, h 1
  • Ding Ding — 2 papers, h 3
  • Ding Ding — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedTransferable Learned Image Compression-Resistant Adversarial Perturbations

1 citations · 1 across the 4 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.