About
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Hi! I’m Zhangkai NI (倪张凯), a Ph.D. candidate at the Department of Computer Science, City University of Hong Kong (HKCityU-CS), jointly supervised by Prof. Sam Tak Wu KWONG (Chair Professor of CS, HKCityU-CS, IEEE Fellow) and Dr. Shiqi WANG (Assistant Professor at HKCityU-CS). Over the years, I was lucky to have some amazing collaborators who have helped me along the way. I am fortunate to have opportunities to work closely with Prof. Kai-Kuang MA (Professor at NTU, IEEE Fellow), Prof. Huanqiang ZENG (Professor at HQU), Dr. Lin MA (Principal Researcher at Tencent AI Lab), and Dr. Wenhan YANG (Postdoctoral Fellow at CityU). My research interests include computer vision, machine learning, and image processing. My current research is focused on generative modeling, unsupervised learning and image quality assessment. “Facing the Sea With Spring Blossoms.” --Hai Zi |
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News
- 2020.02: One paper on color image demosaicking was accepted to IEEE T-IP.
- 2019.04: Received the excellent master thesis award from the Chinese Institute of Electronics (CIE).
- 2018.09: I join the SAM's Group, directed by Prof. Sam Tak Wu KWONG.
- 2018.07: One paper on SCI quality assessment was accepted to IEEE T-CSVT.
- 2018.06: One paper on SCI quality assessment was accepted to IEEE T-IP.
- 2017.06: One paper on SCI quality assessment was accepted to IEEE T-IP.
Selected Publications
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Color Image Demosaicing Using Progressive Collaborative Representation Zhangkai Ni, Kai-Kuang Ma, Huanqiang Zeng, and Baojiang Zhong. IEEE Transactions on Image Processing (T-IP), vol. 29, pp. 4952-4964, March 2020. Abstract | Paper | Code | BibTex Abstract: In this paper, a progressive collaborative representation (PCR) framework is proposed that is able to incorporate any existing color image demosaicing method for further boosting its demosaicing performance. Our PCR consists of two phases: (i) offline training and (ii) online refinement. In phase (i), multiple training-and-refining stages will be performed. In each stage, a new dictionary will be established through the learning of a large number of feature-patch pairs, extracted from the demosaicked images of the current stage and their corresponding original full-color images. After training, a projection matrix will be generated and exploited to refine the current demosaicked image. The updated image with improved image quality will be used as the input for the next training-and-refining stage and performed the same processing likewise. At the end of phase (i), all the projection matrices generated as above-mentioned will be exploited in phase (ii) to conduct online demosaicked image refinement of the test image. Extensive simulations conducted on two commonly-used test datasets (i.e., the IMAX and Kodak) for evaluating the demosaicing algorithms have clearly demonstrated that our proposed PCR framework is able to constantly boost the performance of any image demosaicing method we experimented, in terms of the objective and subjective performance evaluations.
@article{ni2020color,
title={Color Image Demosaicing Using Progressive Collaborative Representation},
author={Zhangkai Ni, Kai-Kuang Ma, Huanqiang Zeng, Baojiang Zhong},
journal={IEEE Transactions on Image Processing},
volume={29},
number={1},
pages={4952--4964},
year={2020},
publisher={IEEE}
}
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A Gabor Feature-Based Quality Assessment Model for the Screen Content Images Zhangkai Ni, Huanqiang Zeng, Lin Ma, Junhui Hou, Jing Chen, and Kai-Kuang Ma. IEEE Transactions on Image Processing (T-IP), vol. 27, no. 9, pp. 4516-4528, September 2018. Abstract | Paper | Code | Project | BibTex Abstract: In this paper, an accurate and efficient full-reference image quality assessment (IQA) model using the extracted Gabor features, called Gabor feature-based model (GFM), is proposed for conducting objective evaluation of screen content images (SCIs). It is well-known that the Gabor filters are highly consistent with the response of the human visual system (HVS), and the HVS is highly sensitive to the edge information. Based on these facts, the imaginary part of the Gabor filter that has odd symmetry and yields edge detection is exploited to the luminance of the reference and distorted SCI for extracting their Gabor features, respectively. The local similarities of the extracted Gabor features and two chrominance components, recorded in the LMN color space, are then measured independently. Finally, the Gabor-feature pooling strategy is employed to combine these measurements and generate the final evaluation score. Experimental simulation results obtained from two large SCI databases have shown that the proposed GFM model not only yields a higher consistency with the human perception on the assessment of SCIs but also requires a lower computational complexity, compared with that of classical and state-of-the-art IQA models.
@article{ni2018gabor,
title={A Gabor feature-based quality assessment model for the screen content images},
author={Ni, Zhangkai and Zeng, Huanqiang and Ma, Lin and Hou, Junhui and Chen, Jing and Ma, Kai-Kuang},
journal={IEEE Transactions on Image Processing},
volume={27},
number={9},
pages={4516--4528},
year={2018},
publisher={IEEE}
}
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Screen Content Image Quality Assessment Using Multi-Scale Difference of Gaussian Ying Fu, Huanqiang Zeng, Lin Ma, Zhangkai Ni, Jianqing Zhu, and Kai-Kuang Ma. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), vol. 28, no. 9, pp. 2428-2432, September 2018. Abstract | Paper | Code | BibTex Abstract: In this paper, a novel image quality assessment (IQA) model for the screen content images (SCIs) is proposed by using multi-scale difference of Gaussian (MDOG). Motivated by the observation that the human visual system (HVS) is sensitive to the edges while the image details can be better explored in different scales, the proposed model exploits MDOG to effectively characterize the edge information of the reference and distorted SCIs at two different scales, respectively. Then, the degree of edge similarity is measured in terms of the smaller-scale edge map. Finally, the edge strength computed based on the larger-scale edge map is used as the weighting factor to generate the final SCI quality score. Experimental results have shown that the proposed IQA model for the SCIs produces high consistency with human perception of the SCI quality and outperforms the state-of-the-art quality models.
@article{fu2018screen,
title={Screen content image quality assessment using multi-scale difference of gaussian},
author={Fu, Ying and Zeng, Huanqiang and Ma, Lin and Ni, Zhangkai and Zhu, Jianqing and Ma, Kai-Kuang},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
volume={28},
number={9},
pages={2428--2432},
year={2018},
publisher={IEEE}
}
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ESIM: Edge Similarity for Screen Content Image Quality Assessment Zhangkai Ni, Lin Ma, Huanqiang Zeng, Jing Chen, Canhui Cai, and Kai-Kuang Ma. IEEE Transactions on Image Processing (T-IP), vol. 26, no. 10, pp. 4818-4831, October 2017. Abstract | Paper | Code | Database | Project | BibTex Abstract: In this paper, an accurate full-reference image quality assessment (IQA) model developed for assessing screen content images (SCIs), called the edge similarity (ESIM), is proposed. It is inspired by the fact that the human visual system (HVS) is highly sensitive to edges that are often encountered in SCIs; therefore, essential edge features are extracted and exploited for conducting IQA for the SCIs. The key novelty of the proposed ESIM lies in the extraction and use of three salient edge features-i.e., edge contrast, edge width, and edge direction. The first two attributes are simultaneously generated from the input SCI based on a parametric edge model, while the last one is derived directly from the input SCI. The extraction of these three features will be performed for the reference SCI and the distorted SCI, individually. The degree of similarity measured for each above-mentioned edge attribute is then computed independently, followed by combining them together using our proposed edge-width pooling strategy to generate the final ESIM score. To conduct the performance evaluation of our proposed ESIM model, a new and the largest SCI database (denoted as SCID) is established in our work and made to the public for download. Our database contains 1800 distorted SCIs that are generated from 40 reference SCIs. For each SCI, nine distortion types are investigated, and five degradation levels are produced for each distortion type. Extensive simulation results have clearly shown that the proposed ESIM model is more consistent with the perception of the HVS on the evaluation of distorted SCIs than the multiple state-of-the-art IQA methods.
@article{ni2017esim,
title={ESIM: Edge similarity for screen content image quality assessment},
author={Ni, Zhangkai and Ma, Lin and Zeng, Huanqiang and Chen, Jing and Cai, Canhui and Ma, Kai-Kuang},
journal={IEEE Transactions on Image Processing},
volume={26},
number={10},
pages={4818--4831},
year={2017},
publisher={IEEE}
}
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Gradient Direction for Screen Content Image Quality Assessment Zhangkai Ni, Lin Ma, Huanqiang Zeng, Canhui Cai, and Kai-Kuang Ma. IEEE Signal Processing Letters (SPL), vol. 23, no. 10, pp. 1394–1398, August 2016. Abstract | Paper | Code | Project | BibTex Abstract: In this letter, we make the first attempt to explore the usage of the gradient direction to conduct the perceptual quality assessment of the screen content images (SCIs). Specifically, the proposed approach first extracts the gradient direction based on the local information of the image gradient magnitude, which not only preserves gradient direction consistency in local regions, but also demonstrates sensitivities to the distortions introduced to the SCI. A deviation-based pooling strategy is subsequently utilized to generate the corresponding image quality index. Moreover, we investigate and demonstrate the complementary behaviors of the gradient direction and magnitude for SCI quality assessment. By jointly considering them together, our proposed SCI quality metric outperforms the state-of-the-art quality metrics in terms of correlation with human visual system perception.
@article{ni2016gradient,
title={Gradient direction for screen content image quality assessment},
author={Ni, Zhangkai and Ma, Lin and Zeng, Huanqiang and Cai, Canhui and Ma, Kai-Kuang},
journal={IEEE Signal Processing Letters},
volume={23},
number={10},
pages={1394--1398},
year={2016},
publisher={IEEE}
}
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Academic Services
- Reviewer
- IEEE Transactions on Image Processing (T-IP)
- IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT)
- Information Sciences (INS)
- Journal of Visual Communication and Image Representation (JVCI)
- IET Image Processing