World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4632
World Ranking
12688
National Ranking
1557

Feng Shao publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Feng Shao sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 241 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Feng Shao D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Feng Shao sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Feng Shao is a researcher affiliated with Ningbo University in China, specializing in computer science and engineering. Their work primarily focuses on areas related to computer vision and pattern recognition, with extensive contributions in media technology as well as environmental and cognitive-related fields.

The main academic fields of Feng Shao's research include:

  • Computer Science
  • Engineering

Within these broader fields, subfields of particular interest in their publications are:

  • Computer Vision and Pattern Recognition
  • Media Technology
  • Environmental Engineering
  • Cognitive Neuroscience
  • Ecology

The thematic topics they have addressed frequently in their work consist of:

  • Advanced Image Fusion Techniques
  • Visual Attention and Saliency Detection
  • Image and Video Quality Assessment
  • Image Enhancement Techniques
  • Advanced Image Processing Techniques
  • Remote-Sensing Image Classification
  • Image and Signal Denoising Methods

Feng Shao has published numerous papers in well-known venues, contributing significantly to specific journals associated with imaging and remote sensing technologies. Most frequent publication venues for their work include:

  • IEEE Transactions on Geoscience and Remote Sensing
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Journal of Visual Communication and Image Representation
  • IEEE Transactions on Instrumentation and Measurement

Among their recent scholarly papers are the following:

  • Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric, 2022, IEEE Transactions on Circuits and Systems for Video Technology
  • A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation, 2020, IEEE Geoscience and Remote Sensing Magazine
  • Unsupervised Decomposition and Correction Network for Low-Light Image Enhancement, 2022, IEEE Transactions on Intelligent Transportation Systems
  • Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric, 2022, IEEE Transactions on Image Processing
  • Vision Transformer for Pansharpening, 2022, IEEE Transactions on Geoscience and Remote Sensing

Collaboration has been a consistent element in Feng Shao's research activities. Frequent co-authors include:

  • Qiuping Jiang
  • Hangwei Chen
  • Xiangchao Meng
  • Xiongli Chai
  • Yo-Sung Ho

Best Publications

  • Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric

    Unknown

  • Perceptual Full-Reference Quality Assessment of Stereoscopic Images by Considering Binocular Visual Characteristics

    Feng Shao;Weisi Lin;Shanbo Gu;Gangyi Jiang

  • Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment

    Qiuping Jiang;Feng Shao;Weisi Lin;Ke Gu

  • Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images

    Qiuping Jiang;Feng Shao;Wei Gao;Zhuo Chen

  • A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation

    Xiangchao Meng;Yiming Xiong;Feng Shao;Huanfeng Shen

  • Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric

    Unknown

  • Unsupervised Decomposition and Correction Network for Low-Light Image Enhancement

    Unknown

  • Asymmetric Coding of Multi-View Video Plus Depth Based 3-D Video for View Rendering

    Feng Shao;Gangyi Jiang;Mei Yu;Ken Chen

  • Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

    Feng Shao;Kemeng Li;Weisi Lin;Gangyi Jiang

  • CGMDRNet: Cross-Guided Modality Difference Reduction Network for RGB-T Salient Object Detection

    Unknown

  • Toward a Blind Deep Quality Evaluator for Stereoscopic Images Based on Monocular and Binocular Interactions

    Feng Shao;Weijun Tian;Weisi Lin;Gangyi Jiang

  • Two-Branch Deep Neural Network for Underwater Image Enhancement in HSV Color Space

    Junkang Hu;Qiuping Jiang;Runmin Cong;Wei Gao

  • Joint Bit Allocation and Rate Control for Coding Multi-View Video Plus Depth Based 3D Video

    Feng Shao;Gangyi Jiang;Weisi Lin;Mei Yu

  • Subjective quality analyses of stereoscopic images in 3DTV system

    Junming Zhou;Gangyi Jiang;Xiangying Mao;Mei Yu

  • New fragile watermarking method for stereo image authentication with localization and recovery

    Mei Yu;Jing Wang;Gangyi Jiang;Zongju Peng

  • BLIQUE-TMI: Blind Quality Evaluator for Tone-Mapped Images Based on Local and Global Feature Analyses

    Qiuping Jiang;Feng Shao;Weisi Lin;Gangyi Jiang

  • A SAR-to-Optical Image Translation Method Based on Conditional Generation Adversarial Network (cGAN)

    Yu Li;Randi Fu;Xiangchao Meng;Wei Jin

  • Blind Image Quality Assessment for Stereoscopic Images Using Binocular Guided Quality Lookup and Visual Codebook

    Feng Shao;Weisi Lin;Shanshan Wang;Gangyi Jiang

  • Three-dimensional visual comfort assessment via preference learning

    Qiuping Jiang;Feng Shao;Gangyi Jiang;Mei Yu

  • Blind Image Quality Measurement by Exploiting High-Order Statistics With Deep Dictionary Encoding Network

    Qiuping Jiang;Wei Gao;Shiqi Wang;Guanghui Yue

  • A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

    Qiuping Jiang;Feng Shao;Gangyi Jiang;Mei Yu

  • Difference of Gaussian statistical features based blind image quality assessment: A deep learning approach

    Yaqi Lv;Gangyi Jiang;Mei Yu;Haiyong Xu

  • No-reference Stereoscopic Image Quality Assessment Using Binocular Self-similarity and Deep Neural Network

    Yaqi Lv;Mei Yu;Gangyi Jiang;Feng Shao

  • Learning Blind Quality Evaluator for Stereoscopic Images Using Joint Sparse Representation

    Feng Shao;Kemeng Li;Weisi Lin;Gangyi Jiang

  • Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images

    Feng Shao;Weisi Lin;Shanshan Wang;Gangyi Jiang

Frequent Co-Authors

Yo-Sung Ho
Yo-Sung Ho Gwangju Institute of Science and Technology
Weisi Lin
Weisi Lin Nanyang Technological University
Qionghai Dai
Qionghai Dai Tsinghua University
Ke Gu
Ke Gu Beijing University of Technology
Shiqi Wang
Shiqi Wang City University of Hong Kong
Shutao Li
Shutao Li Hunan University
Huanfeng Shen
Huanfeng Shen Wuhan University
Sam Kwong
Sam Kwong Lingnan University
Runmin Cong
Runmin Cong Shandong University
Xinghao Ding
Xinghao Ding Xiamen University

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