Dandan Ding

Dandan Ding

Professor at Hangzhou Normal University

👨‍🎓 About Me

I am a Professor at the School of Information Science and Engineering, Hangzhou Normal University, where I lead the Intelligent Video Coding (IVC) Lab. My research primarily focuses on the cutting-edge fields of visual data compression and multimedia communication.

Specifically, my work explores next-generation visual compression technologies, including AV2 and MPEG AI-based point cloud/3DGS compression, as well as their real-time system implementations on NPU and FPGA platforms. Since 2018, I have been continuously supported by Google’s Chrome University Relationship Program to advance the development of the AV2 standard.

I am an IEEE Senior Member and currently serve as an Associate Editor for IEEE Signal Processing Letters. Over the years, I have actively contributed to international standardization, notably receiving the ISO/IEC Appreciation Prize in 2011 for my leadership in MPEG activities.

🔥 News

  • 2026.01:  📢 Serving as Area Chair for ICASSP 2026 and ICME 2026.

  • 2025.10:  📄 Paper on “GeoQE” (Point Cloud Streaming) accepted by ACM MM 2025.

  • 2025.07:  🎉 One paper on LiDAR reflectance compression accepted by ICML 2025.

  • 2025.06:  🚀 Presented “Reno” (Real-time Neural Compression) at CVPR 2025.

  • 2025.01:  📑 Research on Multiscale Point Cloud Compressor published in IEEE TPAMI.

  • 2024.12:  📢 Serving as Area Chair for MMSP 2025.

  • 2024.06:  🏆 Paper “ELIM” nominated as Best Paper Award Finalist at IEEE PCS 2024.

  • 2023.10:  📄 Two papers on G-PCC++ and YOGA accepted by ACM MM 2023.

  • 2020.12:  🏫 Promoted to Professor and lead the IVC Lab at Hangzhou Normal University.

📝 Publications

AAAI 2024
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Another way to the top: exploit contextual clustering in learned image coding
Yichi Zhang, Zhihao Duan, Ming Lu, Dandan Ding, Fengqing Zhu, Zhan Ma

  • We propose Contextual Clustering based LIC (CLIC), which relies on clustering operations and local attention instead of traditional convolutions to generate compact representations for image compression.
TCSVT 2023
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Neural Adaptive Loop Filtering for Video Coding: Exploring Multi-Hypothesis Sample Refinement
Dandan Ding, Junjie Wang, Guangkun Zhen, Debargha Mukherjee, Urvang Joshi, Zhan Ma

  • We reformulate ALF as a Multi-Hypothesis Sample Refinement (MSR) problem, using a DNN model to generate multiple distortion hypotheses that are linearly superimposed to approximate the final reconstruction.
TPAMI 2023
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Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
Jianqiang Wang, Dandan Ding, Zhu Li, Xiaoxing Feng, Chuntong Cao, Zhan Ma

  • We propose SparsePCGC, a low-complexity multiscale representation that performs sparse convolutions only on most-probable positively-occupied voxels to characterize spatial correlations efficiently.
TCyb 2022
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Biprediction-Based Video Quality Enhancement via Learning
Dandan Ding, Wenyu Wang, Junchao Tong, Xinbo Gao, Zoe Liu, Yong Fang

  • We develop a biprediction-based multiframe video enhancement (PMVE) framework that synthesizes virtual frames to extract cross-correlations between successive frames for high-accuracy quality restoration.
Proc. IEEE 2021
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Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies
Dandan Ding, Zhan Ma, Di Chen, Qingshuang Chen, Zoe Liu, Fengqing Zhu

  • This article extensively reviews technical advances in video compression using deep neural networks, presenting case studies on semantic pre-processing, end-to-end neural coding, and neural adaptive post-processing.
TIP 2021
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Neural Reference Synthesis for Inter Frame Coding
Dandan Ding, Xiang Gao, Chenran Tang, Zhan Ma

  • We propose a Neural Reference Synthesis (NRS) framework with joint optimization of reconstruction enhancement and reference synthesis modules to improve both in-ring filtering and inter-frame prediction.
TCSVT 2021
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Point Cloud Upsampling via Perturbation Learning
Dandan Ding, Chi Qiu, Fuchang Liu, Zhigeng Pan

  • We propose learning 2D perturbations through MLPs to estimate coordinate shifts from sparse input points to upsampled dense points, outperforming state-of-the-art methods in geometric uniformity.

Journal Papers

  • Improving Occupancy Prediction for Multiscale Point Cloud Geometry Compression, Z. Li, J. Zhu, D. Ding*, Z. Ma, IEEE TCSVT 2025
  • Revisit Point Cloud Quality Assessment: Current Advances and a Multiscale-Inspired Approach, J. Zhang, T. Chen, D. Ding*, Z. Ma, IEEE TVCG 2025
  • ConPCAC: Conditional Lossless Point Cloud Attribute Compression via Spatial Decomposition, J. Zhang, T. Chen, K. You, D. Ding*, Z. Ma, IEEE TCSVT 2025
  • DeepPCC: Learned Lossy Point Cloud Compression, J. Zhang, G. Liu, J. Zhang, D. Ding*, Z. Ma, IEEE TETCI 2025
  • Learning to Restore Compressed Point Cloud Attribute: A Fully Data-Driven Approach and a Rules-Unrolling-Based Optimization, J. Zhang, J. Zhang, D. Ding*, Z. Ma, IEEE TVCG 2025
  • Scalable Point Cloud Attribute Compression, J. Zhang, J. Wang, D. Ding*, Z. Ma, IEEE TMM 2025
  • A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part I: Geometry, J. Wang, R. Xue, J. Li, D. Ding, Y. Lin, Z. Ma*, IEEE TPAMI 2025
  • A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding – Part II: Attribute, J. Wang, R. Xue, J. Li, D. Ding, Y. Lin, Z. Ma*, IEEE TPAMI 2025
  • Content-aware Rate Control for Geometry-based Point Cloud Compression, J. Zhang, J. Zhang, W. Ma, D. Ding*, Z. Ma, IEEE TCSVT 2024
  • GRNet: Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling, G. Liu, R. Xue, J. Li, D. Ding*, Z. Ma, IEEE TVCG 2024
  • Neural Adaptive Loop Filtering for Video Coding: Exploring Multi-hypothesis Sample Refinement, D. Ding, J. Wang, G. Zhen, D. Mukherjee, U. Joshi, Z. Ma*, IEEE TCSVT 2023
  • Sparse Tensor-Based Multiscale Representation for Point Cloud Geometry Compression, J. Wang, D. Ding, Z. Li, X. Feng, C. Cao, Z. Ma*, IEEE TPAMI 2022
  • Neural Reference Synthesis for Inter Frame Coding, D. Ding*, X. Gao, C. Tang, Z. Ma, IEEE TIP 2022
  • Bi-prediction Based Video Quality Enhancement via Learning, D. Ding, W. Wang, X. Gao, Z. Liu, Y. Fang*, IEEE TCyb 2022
  • Advances in Video Compression Systems Using Deep Neural Networks: A Review and Case Studies, D. Ding, Z. Ma, D. Chen, Q. Chen, Z. Liu, F. Zhu*, Proc. IEEE 2021
  • Point Cloud Upsampling via Perturbation Learning, D. Ding, C. Qiu, F. Liu, Z. Pan*, IEEE TCSVT 2021

Conference Papers

  • GeoQE: Enhancing Quality of Experience in Point Cloud Streaming, J. Zhang, C. Han, D. Ding*, Z. Ma, ACM MM 2025
  • Efficient LiDAR Reflectance Compression via Scanning Serialization, J. Zhu, K. You, D. Ding*, Z. Ma, ICML 2025
  • Reno: Real-time Neural Compression for 3D LiDAR Point Clouds, K. You, T. Chen, D. Ding, M. S. Asif, Z. Ma, CVPR 2025
  • Compressing 3D Gaussian Splatting via a Generalizable Neural Coder, J. Zhang, T. Chen, H. Zhu, D. Wang, D. Ding, Z. Ma, IEEE VCIP 2024
  • ELIM: Extremely Low-Complexity Implicit Neural Model for Super Resolution-Based Coding, W. Wang, J. Wang, D. Ding*, IEEE PCS 2024 (Best Paper Award Finalist)
  • Encoding Auxiliary Information to Restore Compressed Point Cloud Geometry, G. Liu, J. Zhu, D. Ding*, Z. Ma, IJCAI 2024
  • Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding, Y. Zhang, Z. Duan, M. Lu, D. Ding*, F. Zhu, Z. Ma, AAAI 2024
  • YOGA: Yet Another Geometry-based Point Cloud Compressor, J. Zhang, T. Chen, D. Ding*, Z. Ma, ACM MM 2023
  • G-PCC++: Enhanced Geometry-based Point Cloud Compression, J. Zhang, T. Chen, D. Ding*, Z. Ma, ACM MM 2023
  • Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction, J. Wang, D. Ding, Z. Ma, IEEE DCC 2023
  • Low-Light Raw Image Enhancement Using Paired Fast Fourier Convolution and Transformer, Y. Zhang, H. Liu, D. Ding*, Z. Ma, IEEE VCIP 2022
  • PCGFormer: Lossy Point Cloud Geometry Compression via Local Self-Attention, G. Liu, J. Wang, D. Ding*, Z. Ma, IEEE VCIP 2022
  • Quadtree-based Guided CNN for AV1 In-loop Filtering, J. Wang, G. Ding, D. Ding*, D. Mukherjee, U. Joshi, Y. Chen, IEEE ICIP 2022
  • Multiscale Point Cloud Geometry Compression, J. Wang, D. Ding, Z. Li, Z. Ma*, IEEE DCC 2021
  • Guided CNN Restoration with Explicitly Signaled Linear Combination, L. Kong, D. Ding*, D. Mukherjee, U. Joshi, Y. Chen, IEEE ICIP 2020

🎖 Honors and Awards

  • 2024.06 Best Paper Award Finalist, IEEE PCS 2024.

  • 2011.06 ISO/IEC Appreciation Prize, for leadership in MPEG standardization.

📖 Educations

  • 2006.09 - 2011.06, Zhejiang University, China. Ph.D. in Communication and Information System.

  • 2007.07 - 2008.05, EPFL, Switzerland. Joint Ph.D. program in GR-LSM.

  • 2002.09 - 2006.06, Zhejiang University, China. B.S. in Communication Engineering.

💻 Professional Experience

  • 2020.12 - Present, Professor, Hangzhou Normal University. Lead IVC lab.

  • 2015.12 - 2020.11, Assistant Professor, Hangzhou Normal University.

  • 2013.07 - 2015.11, Assistant Professor, Zhejiang University.