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Dr. WU Jinjian


School of Artifical Intelligence,

Xidian University






Tel: 86-29-88202265


Jinjian Wu received his B.Sc and Ph.D from Xidian University, Xi'an, China, in 2008 and 2013, respectively. From September 2011 to August 2014, he was a research assistant/postdoctoral research fellow in Nanyang Technological University. Currently, he is an associated professor with the School of Artificial Intelligence, Xidian University.

His research interests include visual perceptual modeling, saliency estimation, quality evaluation, and just noticeable difference estimation. He has served as the Special Section Chair/Area Chair for PCM2015, ChinaSIP2015, QoMEX2016, and VCIP2017; and served as the TPC member for ICME2014-2015, PCM2015-2016, ICIP2015, QoMEX2016, and NCIG2018. He received the best student paper award of ISCAS 2013, the rising scientist star of Shaanxi province 2017, and the national natural science award (second class) of China 2017.


  • Jinjian Wu, et al., "No-Reference Image Quality Assessment with Orientation Selectivity Mechanism", accepted by IEEE ICIP 2017.
  • Jinjian Wu, et al., "Enhanced Just Noticeable Difference Model for Images with Pattern Complexity", accepted by IEEE Trans. TIP.
  • Xuemei Xie, Yazhong Zhang, Jinjian Wu*, et al, "Bag-of-Words Feature Representation for Blind Image Quality Assessment with Local Quantized Pattern", accepted by Neurocomputing.
  • Wenfei Wan, Jinjian Wu*, et al., "A Novel Just Noticeable Difference Model via Orientation Regularity in DCT Domain", accepted by IEEE Access.






  • Jinjian Wu, et al., "Enhanced Just Noticeable Difference Model for Images with Pattern Complexity", IEEE Trans. On Image Processing, accepted 2017.(PDF(CODE) (Pattern complexity directly affects the perceptual visibility on visual content)  


  • Leida Li, Ya Yan, Zhaolin Lu, Jinjian Wu, Ke Gu, Shiqi Wang, “No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics”, IEEE Access, Vol.5, pp. 2163-2171, 2017.


  • Yuming Fang, Yuan Yuan, Leida Li, Jinjian Wu, Weisi Lin, Zhiqiang Li, “Performance Evaluation of Visual Tracking Algorithms on Video Sequences With Quality Degradation”, IEEE Access, Vol.5, pp.2430-2441, 2017.


  • Weisheng Dong, Guangming Shi, Xin Li, Kefan Peng, Jinjian Wu, Zhenhua Guo, “Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization”, IEEE Transactions on Multimedia, Vol.19, No.2, pp.293-301, February 2017.



  • Jinjian Wu, Weisi Lin, Guangming Shi, Leida Li, “Orientation Selectivity based Visual Pattern for Reduced-Reference Image Quality Assessment”, Information Science, vol.351, pp.18-29, July 2016. (PDF) (CODE(Quality degradation is effectively represented by changes on several visual patterns) 


  • Jinjian Wu, Weisi Lin, Yuming Fang, Leida Li, Guangming Shi, and Issac Niwas, “Visual Structural Degradation based Reduced-Reference Image Quality Assessment”, Signal Processing: Image Communication, Vol. 47, PP. 16–27, Sep. 2016.(PDF)


  • Jinjian Wu, Weisi Lin, and Guangming Shi, “A Survey of Visual Just Noticeable Difference Estimation”, Frontiers of Computer Science, accepted.



  • Leida Li, Dong Wu, Jinjian Wu, Haoliang Li, Weisi Lin, Alex C. Kot, “Image Sharpness Assessment by Sparse Representation”, IEEE Transactions on Multimedia, Vol.18, No.6, pp.1085-1097, June 2016.


  • Weisheng Dong, Fazuo Fu, Guangming Shi, Xun Cao, Jinjian Wu, Guangyu Li, and Xin Li, “HyperspectralImage Super-Resolution via Non-Negative Structured Sparse Representation”, IEEE Transactions on Image Processing, Vol.25, No.5, pp.2337-2352, May 2016.


  • Leida Li, Yu Zhou, Weisi Lin, Jinjian Wu, Xinfeng Zhang, Beijing Chen, “No-reference quality assessment of deblocked images”, Neurocomputing, Vol.177, pp.572-584, February 2016.


  • Jinjian Wu, Guangming Shi, Man Zhang, and Guanmi Chen, “Visual Information Measurement with Quality Assessment”, IEEE VCIP2016, Chengdu, China. (Oral) (Invited Paper)


  • Jinjian Wu, Wenfei Wan, and Guangming Shi, “Content Complexitybased Just Noticeable Difference Estimation in DCT Domain”, APSIPA ASC 2016, Jeju, Korea.(Oral) (Invited Paper)


  • Jinjian Wu, Guangming Shi, Weisi Lin, and C.-C. Jay Kuo, “Enhanced Just Noticeable Difference Model with Visual Regularity Consideration”, IEEE ICASSP 2016, Shanghai, China. (PDF) 



  • Jinjian Wu, Weisi Lin, Guangming Shi, Yazhong Zhang, Weisheng Dong, Zhibo Chen, “Visual Orientation Selectivity based Structure Desc[ant]ription”, IEEE Transactions on Image Processing, Vol.24, No.11, pp.4602-4613, Nov.2015. (PDF) (CODE(The primary visual cortex presents orientation selectivity for visual structure extraction) 


  • Jinjian Wu, Guangming Shi, Weisi Lin, and Xiaotian Wang. “Reduced-Reference Image Quality Assessment with Orientation Selectivity based Visual Pattern”, IEEE ChinaSIP 2015, Chengdu, Sichuan. (Oral) (PDF) (Invited Paper)


  • Jinjian Wu, Leida Li, Guangming Shi, Weisi Lin, and Wenfei Wan, “Visual Pattern Degradation based Image Quality Assessment”, The 2015 International Conference on Optical Instrument and Technology (OIT’2015), Beijing, China. (Oral) (PDF) (Invited Paper)



  • Jinjian Wu, Weisi Lin, Guangming Shi, and Jimin Xiao, “Correlation Based Universal Image/Video Coding Loss Recovery”,Journal of Visual Communication and Image Representation, Vol.25, No.7, pp.1507-1515, October 2014. (PDF(Using the redundancy among pixels to recover coding loss)


  • Jinjian Wu, Weisi Lin, and Guangming Shi,“Image Quality Assessment with Degradation on Spatial Structure”,IEEE Signal Processing Letter, Vol.21, No.4, pp.437-440, Apr.2014. (PDF(Besides degradations on luminance contrast, the HVS is also sensitive to changes on spatial distribution)


  • Jinjian Wu, Weisi Lin, Guangming Shi, Yazhong Zhang, and Lu Liu, “Orientation Selectivity based Structure for Texture Classification”, SPIE /COS Photonics Asia Conference, 2014, Beijing, China.


  • Jinjian Wu, Weisi Lin, and Guangming Shi, “Structural uncertainty based just noticeable difference estimation”, IEEE DSP 2014, Hongkong, China.(Oral) (PDF) (Invited Paper)


  • Jinjian Wu, Weisi Lin, Guangming Shi, and Long Xu. “Reduced-reference image quality assessment with local binary structural pattern”, IEEE ISCAS 2014, Melbourne, Australia. (Oral) (PDF)



  • Jinjian Wu, Weisi Lin, Guangming Shi, Xiaotian Wang, and Fu Li,“Pattern Masking Estimation in Image with Structural Uncertainty”,IEEE Transactions On Image Processing, Vol.22, No.12, pp.4892-4904, Dec.2013. (PDF) (CODE(Structural uncertainty is another factor that determines the visual masking effect)


  • Jinjian Wu, Guangming Shi, Weisi Lin, Anmin Liu, and Fei Qi,“Just Noticeable Difference Estimation For Images with Free-Energy Principle”,IEEE Transactions On Multimedia,Vol.15, No.7, pp.1705-1710, Nov.2013. (PDF) (CODE) (A fresh angle to estimate JND threshold with the theory support from the latest neuroscience finding)


  • Jinjian Wu, Weisi Lin, Guangming Shi, and Anmin Liu, “Reduced-Reference Image Quality Assessment with Visual Information Fidelity”, IEEE Transactions On Multimedia,Vol.15, No. 7, pp.1700-1705, Nov.2013. (PDF(Effectively measure image quality with only 2 values of the reference data)


  • Jinjian Wu, Weisi Lin, Guangming Shi, and Anmin Liu, “Perceptual Quality Metric with Internal Generative Mechanism”, IEEE Transactions on Image Processing, Vol.22, No.1, pp.43-54, Jan.2013. (PDF) (CODE(Our eyes distinguishes orderly and disorderly visual signals, so does our quality metric) (ESI高被引论文)


  • Jinjian Wu, Weisi Lin, and Guangming Shi. “Visual masking estimation based on structural uncertainty”, IEEE ISCAS 2013, Beijing, China.(Oral) (PDF) (Best Student Paper Award)


2012 and before:

  • Jinjian Wu, Fei Qi, and GuangmingShi,“Non-local spatial redundancy reduction for bottom-up saliency estimation,” Journal of Visual Communication and Image Representation, Vol.23, No.7, pp.1158-1166, October 2012.  (PDF) (CODE(The HVS is adapt to summarize regular visual content, while poor for irregular content)


  • Jinjian Wu, Fei Qi, and GuangmingShi,“Self-similarity based structural regularity for just noticeable difference estimation”, Journal of Visual Communication and Image Representation, Vol.23, No.6, pp.845-852, August 2012. (PDF(Saliency is measured with the quantity of visual information/visual entropy)


  • Jinjian Wu, Fei Qi, and Guangming Shi, “Image quality assessment based on improved structural similarity”, PCM 2012, Singapore. (Oral) (PDF)


  • Jinjian wu, Fei Qi, and Guangming Shi, “Unified spatial masking for just-noticeable difference estimation”, IEEE APSIPA ASC, Oct.2011. Xi’an, China. (PDF)


  • Jinjian wu, Fei Qi, and Guangming Shi, “An improved model of pixel adaptive just-noticeable difference estimation”, IEEE ICASSP, Mar. 2010, Dallas, Texas, USA. (PDF)


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