Nick Johnston

3.7k total citations · 1 hit paper
10 papers, 785 citations indexed

About

Nick Johnston is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computational Mechanics. According to data from OpenAlex, Nick Johnston has authored 10 papers receiving a total of 785 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 2 papers in Signal Processing and 2 papers in Computational Mechanics. Recurrent topics in Nick Johnston's work include Advanced Data Compression Techniques (3 papers), Video Coding and Compression Technologies (2 papers) and Advanced Vision and Imaging (2 papers). Nick Johnston is often cited by papers focused on Advanced Data Compression Techniques (3 papers), Video Coding and Compression Technologies (2 papers) and Advanced Vision and Imaging (2 papers). Nick Johnston collaborates with scholars based in United States, Ireland and United Kingdom. Nick Johnston's co-authors include David Minnen, Sung Jin Hwang, George Toderici, Johannes Ballé, Kevin Murphy, George Papandreou, Nathan Silberman, Vivek Rathod, Jonathan Huang and Austin Myers and has published in prestigious journals such as SHILAP Revista de lepidopterología, Gastrointestinal Endoscopy and arXiv (Cornell University).

In The Last Decade

Nick Johnston

8 papers receiving 759 citations

Hit Papers

Im2Calories: Towards an Automated Mobile Vision Food Diary 2015 2026 2018 2022 2015 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nick Johnston United States 5 521 183 182 178 58 10 785
Amaia Salvador Spain 8 456 0.9× 47 0.3× 87 0.5× 90 0.5× 173 3.0× 13 675
Insoo Woo United States 9 229 0.4× 30 0.2× 274 1.5× 165 0.9× 36 0.6× 12 801
Ashutosh Singla Germany 9 283 0.5× 57 0.3× 53 0.3× 59 0.3× 23 0.4× 22 426
W. R. Sam Emmanuel India 13 283 0.5× 25 0.1× 52 0.3× 60 0.3× 78 1.3× 45 613
Anoop Korattikara United States 6 104 0.2× 9 0.0× 182 1.0× 173 1.0× 152 2.6× 9 490
Simone Di Domenico Italy 11 98 0.2× 133 0.7× 12 0.1× 110 0.6× 46 0.8× 23 521
Yoshiyuki Kawano Japan 8 244 0.5× 16 0.1× 354 1.9× 402 2.3× 44 0.8× 13 735
Chun Pong Lau United States 9 130 0.2× 20 0.1× 40 0.2× 75 0.4× 72 1.2× 23 274
Yoko Yamakata Japan 10 125 0.2× 29 0.2× 33 0.2× 47 0.3× 128 2.2× 66 338

Countries citing papers authored by Nick Johnston

Since Specialization
Citations

This map shows the geographic impact of Nick Johnston'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 Nick Johnston with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nick Johnston more than expected).

Fields of papers citing papers by Nick Johnston

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nick Johnston. 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 Nick Johnston. The network helps show where Nick Johnston may publish in the future.

Co-authorship network of co-authors of Nick Johnston

This figure shows the co-authorship network connecting the top 25 collaborators of Nick Johnston. A scholar is included among the top collaborators of Nick Johnston based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nick Johnston. Nick Johnston is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Minnen, David & Nick Johnston. (2023). Advancing the Rate-Distortion-Computation Frontier for Neural Image Compression. arXiv (Cornell University). 2940–2944. 4 indexed citations
2.
Shor, Joel & Nick Johnston. (2023). DOES VIDEO COMPRESSION AFFECT CADE POLYP DETECTORS?. Gastrointestinal Endoscopy. 97(6). AB768–AB768.
3.
Chou, Philip A., et al.. (2022). LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks. SHILAP Revista de lepidopterología. 2. 20 indexed citations
4.
Ellis, Geraint, et al.. (2020). Public Support for Renewables. Research Portal (Queen's University Belfast). 197–203.
5.
Agustsson, Eirikur, David Minnen, Nick Johnston, et al.. (2020). Scale-Space Flow for End-to-End Optimized Video Compression. 8500–8509. 176 indexed citations
6.
Ballé, Johannes, Nick Johnston, & David Minnen. (2019). Integer Networks for Data Compression with Latent-Variable Models. International Conference on Learning Representations. 37 indexed citations
7.
Baluja, Shumeet, David Marwood, Nick Johnston, & Michele Covell. (2019). Learning to Render Better Image Previews. 7. 1700–1704. 2 indexed citations
8.
Chinen, Troy, Johannes Ballé, Chunhui Gu, et al.. (2018). Towards A Semantic Perceptual Image Metric. 624–628. 3 indexed citations
9.
Johnston, Nick, Damien Vincent, David Minnen, et al.. (2018). Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks. 4385–4393. 223 indexed citations
10.
Myers, Austin, Nick Johnston, Vivek Rathod, et al.. (2015). Im2Calories: Towards an Automated Mobile Vision Food Diary. 1233–1241. 320 indexed citations breakdown →

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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