Ajit Bopardikar

559 citations
26 papers · 409 · h-index 7

Impact in

Papers in

Ajit Bopardikar

20 papers receiving 387 citations

Peers

Ajit Bopardikar
Comparison fields: 5 of 75
  • Cognitive Neuroscience 254
  • Signal Processing 110
  • Cellular and Molecular Neuroscience 64
  • Ophthalmology 23
  • Computer Vision and Pattern Recognition 49
Replace Valérie Louis-Dorr with:
Valérie Louis-Dorr France
Chang’an A. Zhan China
Marina Ronzhina Czechia
Accardo Agostino Italy
Yongshuo Zong China
Sebastian Stober Germany
Ömer Türk Türkiye
Vipin Gupta India
Andrzej W. Przybyszewski United States
Ratna Yanti Singapore
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Citations per field
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Citations per year

Countries citing papers authored by Ajit Bopardikar

Since Specialization
Citations

This map shows the geographic impact of Ajit Bopardikar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ajit Bopardikar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ajit Bopardikar more than expected).

Fields of papers citing papers by Ajit Bopardikar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ajit Bopardikar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ajit Bopardikar. The network helps show where Ajit Bopardikar may publish in the future.

Co-authors

The 22 scholars most cited alongside Ajit Bopardikar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ajit Bopardikar Line = papers co-authored together Ajit Bopardikar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1999278
2 201136
3 201118
4 200211
5 201811
6 202110
7 20109
8 20106
9 20244
10
A Perceptual No-Reference Blockiness Metric for JPEG Images.
20044
11 20104
12 19974
13 20193
14 20173
15 20052
16 20002
17 20181
18 20041
19 20141
20 20161

About Ajit Bopardikar

Ajit Bopardikar is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Molecular Biology, Artificial Intelligence and Cognitive Neuroscience, having authored 26 papers that have together received 409 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (7 papers), Advanced Data Compression Techniques (5 papers), Digital Filter Design and Implementation (5 papers), Genomics and Phylogenetic Studies (4 papers), Ophthalmology and Visual Impairment Studies (4 papers), Algorithms and Data Compression (4 papers), Image and Video Quality Assessment (3 papers) and Advanced Vision and Imaging (3 papers). The work is most often cited by research in Cognitive Neuroscience (254 citations), Signal Processing (110 citations), Cellular and Molecular Neuroscience (64 citations), Ophthalmology (23 citations) and Computer Vision and Pattern Recognition (49 citations). Ajit Bopardikar has collaborated with scholars based in India, United States and South Korea. Frequent co-authors include Raghuveer Rao, Kenneth P. Swartz, Vincent J. Samar, Deep Bera, TaeJin Ahn, Kyusang Lee, Vijay N. Tiwari, Andrew Perkis, Zia S. Pradhan and Venkataraman Parthasarathy. Their work appears in journals such as Brain and Language, Journal of Glaucoma, Bioinformatics, IEEE Transactions on Signal Processing and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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