Liu, Ying - School of Engineering - Santa Clara University

Biography

Dr. Ying Liu is an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University. She graduated from the Department of Electrical Engineering, The State University of New York at Buffalo. Her main research interests are in deep learning-based image and video processing and coding, coding for machines, point cloud coding, vision-language models, and generative AI. She is a member of IEEE and a member of SPIE. Her research articles are published in prestigious journals such as the IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Consumer Electronics, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Pattern Recognition (Elsevier), and many flagship conferences. She serves as an Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology, and she also serves as the technical program committee member of multiple international conferences. She is the Secretary/Treasurer of the Asia-Pacific Signal and Information Processing Association (APSIPA) US Chapter.

Education

Ph.D., Electrical Engineering, SUNY at Buffalo, 2012

M.S., Electrical Engineering, SUNY at Buffalo, 2008

B.S., Telecommunications Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China, 2006

Research

1. Image and Video Processing, Computer Vision, Deep Learning
2. Image and Video Coding, Coding for Machines, Point Cloud Coding
3. Vision-Language Model, Generative AI

Courses Taught

COEN 166/266 Artificial Intelligence
COEN 140 Machine Learning and Data Mining
COEN 194/195/196 Senior Design Project
COEN 240 Machine Learning
COEN 347 Advanced Techniques in Video Coding

Awards

1. Researcher of the Year Award, School of Engineering, Santa Clara University, 2024.

2. Ying Liu (PI), “ERI: Generative Adversarial Networks for Video Coding,” external funding, National Science Foundation, Feb. 1, 2022-Jan. 31, 2025, estimated.

3. Ying Liu (PI), “Learned Video Compression with Generative Adversarial Networks and Transformers,” NVIDIA Academic Hardware Grant Program, Mar. 2022.

4. Nam Ling (PI) and Ying Liu (PI), “Low Complexity and High Efficiency Image and Video Coding with Deep Learning on Heterogeneous Platforms,” external funding, Kwai, Inc, June 16, 2021- June 15, 2022.

5. Nam Ling (PI) and Ying Liu (Co-PI), “Low Complexity and High Efficiency Image and Video Processing with Neural Network on Heterogeneous Platforms,” external funding, Kwai, Inc., June 16, 2020 - June 15, 2021.

6. Ying Liu (PI), “Video Coding for Semantic Segmentation,” Kuehler Undergraduate Research Grant, School of Engineering, Santa Clara University, 2023.

7. Ying Liu (PI), “Image Enhancement Through Transformers,” Kuehler Undergraduate Research Grant, School of Engineering, Santa Clara University, 2022.

8. Ying Liu (PI), Internal Research Grant, School of Engineering, Santa Clara University, 2020/2021.

9. Ying Liu (PI), Internal Research Grant, School of Engineering, Santa Clara University, 2019/2020.

Publications

Journals

P. Du, Y. Liu, N. Ling, “CGVC-T: contextual generative video compression with transformers,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Apr. 2024.
M. Mathai, Y. Liu, N. Ling, “A hybrid transformer-LSTM model with 3D separable convolution for video prediction,” IEEE Access, pp. 39589 - 39602, Mar. 2024.
M. G. Schimpf, N. Ling, Y. Liu, “Compressing of medium- to low-rate transform residuals with semi-extreme sparse coding as an alternate transform in video,” IEEE Transactions on Consumer Electronics, Mar. 2023.
F. Akhyar, Y. Liu, C.‑Y. Hsu, T. K. Shih, C.‑Y. Lin, “FDD: a deep learning-based steel defect detector,” The International Journal of Advanced Manufacturing Technology, vol. 126, pp. 1093 - 1107, Mar. 2023.
B. Hou, Y. Liu, N. Ling, Y. Ren, L. Liu, “A survey of efficient deep learning models for moving object segmentation,” APSIPA Transactions on Signal and Information Processing, vol. 12, no. 1, pp. 1 - 84, Jan. 2023.
B. Hou, Y. Liu, N. Ling, L. Liu, Y. Ren, “A fast lightweight 3D separable convolutional neural network with multi-input multi-output for moving object detection,” IEEE Access, vol. 9, pp. 148433 - 148448, Oct. 2021.
Y. Liu, K. Tountas, D. A. Pados, S. N. Batalama, and M. J. Medley, “L1-subspace tracking for streaming data,” Elsevier Journal of Pattern Recognition, 2019.
Y. Liu and J. Kim, “Variable block-size compressed sensing for depth map coding,” Multimedia Tools and Applications, Apr. 2019.

Conference Proceedings

T. Shen, W.-H. Peng, H.-C. Shih, Y. Liu, “Learning-based conditional image compression,” IEEE Int. Symp. Circuits and Systems (ISCAS), Singapore, May 2024.
Z. Zhang and Y. Liu, “Redundancy removal module for reducing the bitrates of image coding for machines,” IEEE Int. Symp. Circuits and Systems (ISCAS), Singapore, May 2024.
Y. Pei, Y. Liu, N. Ling, “MobileViT-GAN: a generative model for low bitrate image coding,” IEEE Conf. Visual Commun. and Image Process. (VCIP), Jeju, Korea, Dec. 2023.
T. Shen, Y. Liu, “Learned image compression with transformers,” SPIE Defense + Commercial Sensing, Conference: Big Data V: Learning, Analytics, and Applications, Orlando, FL, May 2023.
Yifei Pei, Ying Liu, Nam Ling, Yongxiong Ren, Lingzhi Liu, “An end-to-end deep generative network for low bitrate image coding,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Monterey, CA, May 2023.
Tianma Shen, Ying Liu, “Learned image compression with transformers,” in Proceedings of the SPIE Defense + Commercial Sensing, Conference 12522, Big Data V: Learning, Analytics, and Applications, Orlando, FL, May 2023.
Pengli Du, Ying Liu, Nam Ling, Yongxiong Ren, and Lingzhi Liu, “Generative video compression with a transformer-based discriminator,” in Proceedings of the Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
Zhongpeng Zhang, Ying Liu, “Side information driven image coding for machines,” in Proceedings of the Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
Mareeta Mathai, Ying Liu, Nam Ling, “A lightweight model with separable CNN and LSTM for video prediction,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, May-June 2022.
Pengli Du, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming-Kai Hsu, “A generative adversarial network for video compression,” in Proceedings of the SPIE Defense + Commercial Sensing, Conference: Big Data IV: Learning, Analytics, and Applications, Orlando, Florida, Apr. 2022.
Bingxin Hou, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming-Kai Hsu, “F3DsCNN: a fast two-branch 3D separable CNN for moving object detection,” in Proceedings of the IEEE Conference on Visual Communications and Image Processing (VCIP), Munich, Germany, Dec. 2021.
Yifei Pei, Ying Liu, Nam Ling, Lingzhi Liu, and Yongxiong Ren, “Class-specific neural network for video compressed sensing,” in Proceedings of the IEEE International Symposium on Circuits and Systems, Daegu, Korea, May 2021.
Ying Liu, Pengli Du, and Yuzhu Li, “Hierarchical motion-compensated deep network for video compression,” in Proceedings of the SPIE Symposium on Defense + Commercial Sensing, Conference 11730, Big Data III: Learning, Analytics, and Applications, Orlando, FL, Apr. 2021.
Michael Schimpf, Nam Ling, Yunhui Shi, and Ying Liu, “Sparse coding of intra prediction residuals for screen content coding,” in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), 2021.

Thesis

Decoding of Purely Compressed Sensed Video
Y. Liu (advisor: Prof. Dimitris A. Pados), Ph.D. Thesis, Department of Electrical Engineering, SUNY Buffalo, June 2012.