Marcus Barkowsky

2.2k total citations
69 papers, 957 citations indexed

About

Marcus Barkowsky is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Cognitive Neuroscience. According to data from OpenAlex, Marcus Barkowsky has authored 69 papers receiving a total of 957 indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Computer Vision and Pattern Recognition, 21 papers in Signal Processing and 18 papers in Cognitive Neuroscience. Recurrent topics in Marcus Barkowsky's work include Image and Video Quality Assessment (52 papers), Video Coding and Compression Technologies (21 papers) and Advanced Optical Imaging Technologies (18 papers). Marcus Barkowsky is often cited by papers focused on Image and Video Quality Assessment (52 papers), Video Coding and Compression Technologies (21 papers) and Advanced Optical Imaging Technologies (18 papers). Marcus Barkowsky collaborates with scholars based in France, Germany and Italy. Marcus Barkowsky's co-authors include Patrick Le Callet, André Kaup, Kjell Brunnström, M Urvoy, Quan Huynh‐Thu, Enrico Masala, Margaret Pinson, Alexander Raake, Pierre Lebreton and Roland Bitto and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Marcus Barkowsky

68 papers receiving 922 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcus Barkowsky France 16 764 349 209 161 157 69 957
Yong Ju Jung South Korea 17 697 0.9× 491 1.4× 262 1.3× 238 1.5× 73 0.5× 63 956
Filippo Speranza Canada 19 933 1.2× 558 1.6× 384 1.8× 242 1.5× 243 1.5× 57 1.3k
Tibor Balogh Hungary 13 385 0.5× 386 1.1× 157 0.8× 149 0.9× 35 0.2× 55 624
Hosik Sohn South Korea 14 429 0.6× 298 0.9× 175 0.8× 124 0.8× 87 0.6× 31 552
Martin Řeřábek Switzerland 16 770 1.0× 173 0.5× 90 0.4× 69 0.4× 241 1.5× 42 862
Norishige Fukushima Japan 15 799 1.0× 181 0.5× 83 0.4× 103 0.6× 223 1.4× 114 1.0k
Patrick Le Callet France 10 786 1.0× 154 0.4× 212 1.0× 126 0.8× 41 0.3× 16 879
Siegmund Pastoor Germany 12 259 0.3× 229 0.7× 143 0.7× 206 1.3× 72 0.5× 18 613
Philip Corriveau United States 10 280 0.4× 125 0.4× 88 0.4× 73 0.5× 61 0.4× 25 387
Philippe Hanhart Switzerland 17 765 1.0× 101 0.3× 66 0.3× 38 0.2× 304 1.9× 52 858

Countries citing papers authored by Marcus Barkowsky

Since Specialization
Citations

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

Fields of papers citing papers by Marcus Barkowsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcus Barkowsky

This figure shows the co-authorship network connecting the top 25 collaborators of Marcus Barkowsky. A scholar is included among the top collaborators of Marcus Barkowsky 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 Marcus Barkowsky. Marcus Barkowsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Servetti, Antonio, et al.. (2024). Multiple Image Distortion DNN Modeling Individual Subject Quality Assessment. ACM Transactions on Multimedia Computing Communications and Applications. 20(8). 1–27. 1 indexed citations
2.
Servetti, Antonio, et al.. (2024). Modeling Subject Scoring Behaviors in Subjective Experiments Based on a Discrete Quality Scale. IEEE Transactions on Multimedia. 26. 8742–8757. 3 indexed citations
3.
Barkowsky, Marcus, et al.. (2022). Mimicking Individual Media Quality Perception with Neural Network based Artificial Observers. ACM Transactions on Multimedia Computing Communications and Applications. 18(1). 1–25. 7 indexed citations
4.
Wallendael, Glenn Van, et al.. (2021). On the Link Between Subjective Score Prediction and Disagreement of Video Quality Metrics. IEEE Access. 9. 152923–152937. 6 indexed citations
5.
Barkowsky, Marcus, et al.. (2020). Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach. Multimedia Tools and Applications. 80(3). 3469–3487. 6 indexed citations
6.
Masala, Enrico, et al.. (2019). Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in VQA datasets using objective measures. Signal Processing Image Communication. 74. 32–41. 6 indexed citations
7.
Barkowsky, Marcus, et al.. (2019). The Golden Bullet: A Comparative Study for Target Acquisition, Pointing and Shooting. 1–8. 7 indexed citations
8.
Barkowsky, Marcus, et al.. (2019). Linking Bitstream Information to QoE: A Study on Still Images Using HEVC Intra Coding. Advances in Electrical and Electronic Engineering. 17(4). 4 indexed citations
9.
Masala, Enrico, et al.. (2018). Improved Performance Measures for Video Quality Assessment Algorithms Using Training and Validation Sets. IEEE Transactions on Multimedia. 21(8). 2026–2041. 8 indexed citations
10.
Masala, Enrico, et al.. (2017). Framework for reproducible objective video quality research with case study on PSNR implementations. Digital Signal Processing. 77. 195–206. 19 indexed citations
11.
Rai, Yashas, Marcus Barkowsky, & Patrick Le Callet. (2016). Role of spatio-temporal distortions in the visual periphery in disrupting natural attention deployment. Electronic Imaging. 28(16). 1–6. 7 indexed citations
12.
Lebreton, Pierre, Alexander Raake, & Marcus Barkowsky. (2016). Studying user agreement on aesthetic appeal ratings and its relation with technical knowledge. 1–6. 3 indexed citations
13.
14.
Barkowsky, Marcus, et al.. (2014). Hybrid video quality prediction: reviewing video quality measurement for widening application scope. Multimedia Tools and Applications. 74(2). 323–343. 13 indexed citations
15.
Leszczuk, Mikołaj, Lucjan Janowski, & Marcus Barkowsky. (2013). Freely available large-scale video quality assessment database in Full-HD resolution with H.264 coding. 3. 1162–1167. 6 indexed citations
16.
Wang, Kun, et al.. (2012). Reproducibility of crosstalk measurements on active glasses 3D LCD displays based on temporal characterization. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8288. 82880Y–82880Y. 8 indexed citations
17.
Wang, Kun, et al.. (2012). Perceived 3D TV Transmission Quality Assessment: Multi-Laboratory Results Using Absolute Category Rating on Quality of Experience Scale. IEEE Transactions on Broadcasting. 58(4). 544–557. 35 indexed citations
18.
Barkowsky, Marcus, et al.. (2006). A new algorithm for reducing the requantization loss in video transcoding. European Signal Processing Conference. 1–5. 1 indexed citations
19.
Barkowsky, Marcus, et al.. (2006). Overview of Low-Complexity Video Transcoding from H.263 to H.264. 49–52. 7 indexed citations
20.
Barkowsky, Marcus, et al.. (2006). Influence of the Presentation Time on Subjective Votings of Coded Still Images. 429–432. 4 indexed citations

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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