Articles in Journals

Published

  1. New: CGVC-T: contextual generative video compression with transformers
    P. Du, Y. Liu, N. Ling, IEEE Journal on Emerging and Selected Topics in Circuits and Systems (Early Access), Apr. 2024.
  2. New: A hybrid transformer-LSTM model with 3D separable convolution for video prediction
    M. Mathai, Y. Liu, N. Ling, IEEE Access, pp. 39589 - 39602, Mar. 2024.
  3. Compressing of medium-to low-rate transform residuals with semi-extreme sparse coding as an alternate transform in video
    M. G. Schimpf, N. Ling, Y. Liu, IEEE Transactions on Consumer Electronics, Apr. 2023.
  4. FDD: a deep learning-based steel defect detectors
    F. Akhyar, Y. Liu, C.-Y. Hsu, T. K. Shih, C.-Y. Lin, The International Journal of Advanced Manufacturing Technology, Mar. 2023.
  5. A survey of efficient deep learning models for moving object segmentation
    B. Hou, Y. Liu, N. Ling, Y. Ren, and L. Liu, APSIPA Trans. Signal and Info. Process., vol. 12, no. 1, Jan. 2023.
  6. A fast lightweight 3D separable convolutional neural network with multi-input multi-output for moving object detection
    B. Hou, Y. Liu, N. Ling, L. Liu, and Y. Ren, IEEE Access, vol. 9, pp. 148433 - 148448, Oct. 2021.
  7. L1-subspace tracking for streaming data
    Y. Liu, K. Tountas, D. A. Pados, S. N. Batalama, and M. J. Medley, Elsevier Journal of Pattern Recognition, 2019, accepted.
  8. Variable block-size compressed sensing for depth map coding
    Y. Liu and J. Kim, Multimedia Tools and Applications, Apr. 2019.
  9. Reconstruction of compressed-sensed multiview video with disparity and motion compensated total-variation minimization
    Y. Liu, D. A. Pados, J. Kim, and C. Zhang, IEEE Trans. Circuits and Systems for Video Tech., vol. 28, pp. 1288-1302, June 2018.
  10. Compressed-sensed-domain L1-PCA Video Surveillance
    Y. Liu, and D. A. Pados, IEEE Trans. Multimedia, vol. 18, pp. 351-363, Mar. 2016.
  11. Decoding of framewise compressed-sensed video via interframe total variation minimization
    Y. Liu and D. A. Pados, SPIE J. Electron. Imaging, Special Issue on Compressive Sensing for Imaging, vol. 22, no. 2, Apr.-Jun. 2013.
  12. Motion-aware decoding of compressed-sensed video
    Y. Liu, M. Li, and D. A. Pados, IEEE Trans. Circuits and Systems for Video Technology, vol. 23, pp. 438-444, Mar. 2013.

Articles in Conference Proceedings

Published/Accepted

  1. New: Learning-based conditional image compression
    T. Shen, W.-H. Peng, H.-C. Shih, Y. Liu, IEEE Int. Symp. Circuits and Systems (ISCAS), Singapore, May 2024, accepted.
  2. New: Redundancy removal module for reducing the bitrates of image coding for machines
    Z. Zhang and Y. Liu, IEEE Int. Symp. Circuits and Systems (ISCAS), Singapore, May 2024, accepted.
  3. MobileViT-GAN: a generative model for low bitrate image coding
    Y. Pei, Y. Liu, N. Ling, IEEE Conf. Visual Commun. and Image Process. (VCIP), Jeju, Korea, Dec. 2023.
  4. Learned image compression with transformers
    T. Shen, Y. Liu, SPIE Defense + Commercial Sensing, Conference: Big Data V: Learning, Analytics, and Applications, Orlando, FL, May 2023.
  5. An end-to-end generative adversarial network for low bitrate image coding
    Y. Pei, Y. Liu, N. Ling, IEEE Int. Symp. Circuits and Systems (ISCAS), Monterey, CA, May 2023.
  6. Generative video compression with a transformer-based discriminator
    P. Du, Y. Liu, N. Ling, Y. Ren, and L. Liu, Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
  7. Side information driven image coding for machines
    Z. Zhang, Y. Liu, Picture Coding Symposium (PCS), San Jose, CA, Dec. 2022.
  8. A lightweight model with separable CNN and LSTM for video prediction
    M. Mathai, Y. Liu, and N. Ling, IEEE Int. Symp. Circuits and Systems (ISCAS), Austin, TX, May-June 2022.
  9. A generative adversarial network for video compression
    P. Du, Y. Liu, N. Ling, L. Liu, Y. Ren, M. Hsu, SPIE Defense + Commercial Sensing, Conference: Big Data IV: Learning, Analytics, and Applications, Orlando, FL, Apr. 2022.
  10. F3DsCNN: a fast two-branch 3D separable CNN for moving object detection
    B. Hou, Y. Liu, N. Ling, L. Liu, Y. Ren, M. Hsu, IEEE Conf. Visual Commun. and Image Process. (VCIP), Munich, Germany, Dec. 2021.
  11. Class-specific neural network for video compressed sensing
    Y. Pei, Y. Liu, N. Ling, L. Liu, Y. Ren, IEEE Int. Symp. Circuits and Systems, Daegu, Korea, May 2021.
  12. Hierarchical motion-compensated deep network for video compression
    Y. Liu, P. Du, Y. Li, SPIE Symp. Defense + Commercial Sensing, Online, Apr. 2021.
  13. Sparse coding of intra prediction residuals for screen content coding
    M. Schimpf, N. Ling, Y. Shi, and Y. Liu, IEEE Int. Conf. Consumer Electronics (ICCE), Online, Jan. 2021.
  14. A super-fast deep network for moving object detection
    B. Hou, Y. Liu, and N. Ling, IEEE Int. Symp. Circuits and Systems, Seville, Spain, May 2020.
  15. Deep learning for block compressed sensing of images in sparse domain
    Y. Pei, Y. Liu, and N. Ling, IEEE Int. Symp. Circuits and Systems, Seville, Spain, May 2020, accepted.
  16. Motion-aware deep video coding network
    R. Khan, Y. Liu, Conference SI110: Big Data II: Learning, Analytics, and Applications, SPIE Defense + Commercial Sensing 2020 , Apr. 2020.
  17. Edge-to-fog computing for color-assisted moving object detection
    Y. Liu, Z. Bellay, P. Bradsky, G. Chandler, and B. Craig, in Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, Baltimore, MD, Apr. 2019.
  18. Conformity evaluation of data samples by L1-norm principal-component analysis
    Y. Liu, and D. A. Pados, SPIE Conf. Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, Orlando, FL, Apr. 2018.
  19. Iterative re-weighted L1-norm principal-component analysis
    Y. Liu, D. A. Pados, S. N. Batalama, and M. J. Medley, in Proc. Asilomar Conference, Pacific Grove, CA, Oct. - Nov. 2017.
  20. Cloud-assisted individual L1-PCA face recognition using wavelet-domain compressed images
    F. Maritato, Y. Liu, S. Colonnese, and D. A. Pados, in Proc. the 6th European Workshop on Visual Information Process. (EUVIP), Marseille, France, Oct. 2016.
  21. Two-stage tensor locality-preserving projection face recognition
    Y. Liu, D. A. Pados, and C.H. Yeh, IEEE Int. Conf. Multimedia Big Data, Taipei, Taiwan, Apr. 2016.
  22. Face recognition with L1-norm subspaces
    F. Maritato, Y. Liu, D. A. Pados, and S. Colonnese, in Proc. SPIE Commercial + Scientific Sensing and Imaging, Baltimore, MD, Apr. 2016.
  23. Video background tracking and foreground extraction via L1-subspace updates
    M. Pierantozzi, Y. Liu, D. A. Pados, and S. Colonnese, in Proc. SPIE Commercial + Scientific Sensing and Imaging, Baltimore, MD, Apr. 2016.
  24. Joint-view Kalman-filter recovery of compressed-sensed multiview videos
    Y. Liu, S. Chamadia, and D. A. Pados, IEEE ICASSP, Shanghai, China, Mar. 2016.
  25. Probing metamaterials with structured light
    Y. Xu, J. Sun, J. Zeng, Z. Kudyshev, A. Pandey, Y. Liu, and N. M. Litchinitser, in Proc. SPIE 9544, Metamaterials, Metadevices, and Metasystems, Sept. 2015.
  26. Compressed-sensed-domain L1-PCA video surveillance
    Y. Liu, D. Pados, SPIE Defense, Security, and Sensing (DSS), Baltimore, MD, Apr. 2015.
  27. Disparity-compensated total-variation minimization for compressed-sensed multiview image reconstruction
    Y. Liu, C. Zhang, and J. Kim, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 2015.
  28. Adaptive measurement rate allocation for block-based compressed sensing of depth maps
    K. R. Vijayanagar, Y. Liu, and J. Kim, International Conference on Image Processing (ICIP), Paris, France, Oct. 2014.
  29. Rate-distortion optimization for compressive video sampling
    Y. Liu, K. R. Vijayanagar, and J. Kim, SPIE Defense, Security, and Sensing (DSS), Baltimore, MD, May 2014.
  30. Quad-tree partitioned compressed sensing for depth map coding,
    Y. Liu, K. R. Vijayanagar, and J. Kim, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florense, Italy, May, 2014.
  31. Rate-adaptive compressive video acquisition with sliding-window total-variation-minimization reconstruction
    Y. Liu and D. A. Pados, Proc. SPIE, Compressive Sensing Conf., SPIE Defense, Security, and Sensing, Baltimore, MD, vol. 8717, May, 2013.
  32. Decoding of purely compressed-sensed video
    Y. Liu, M. Li, and D. A. Pados, Proc. SPIE, Compressive Sensing Conf., SPIE Defense, Security, and Sensing, Baltimore, MD, Apr., 2012.
  33. Motion compensation as sparsity-aware decoding in compressive video streaming
    Y. Liu, M. Li, K. Gao, and D. A. Pados, Proc. 17th Intern. Conf. on Digital Signal Processing (DSP), Corfu, Greece, July, 2011, pp. 1-5.

Theses

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


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