Andreas Sackl

739 citations
43 papers · 569 · h-index 15

Impact in

Papers in

Andreas Sackl

39 papers receiving 552 citations

Peers

Andreas Sackl
Comparison fields: 5 of 76
  • Computer Vision and Pattern Recognition 372
  • Human-Computer Interaction 60
  • Media Technology 81
  • Computer Networks and Communications 165
  • Signal Processing 75
Replace Yong-Ik Yoon with:
Yong-Ik Yoon South Korea
Petteri Alahuhta Finland
Graça Bressan Brazil
Bruno Gardlo Austria
Reuben Edwards United Kingdom
Mohammed Korayem United States
Ekaterina Olshannikova Finland
Nicholas Race United Kingdom
Choonsung Shin South Korea
Hourieh Khalajzadeh Australia
Andreas Sackl relative to Yong-Ik Yoon South Korea Yong-Ik Yoon's profile →
Citations per field
00.5×1.5×2×2.3×
Yong-Ik Yoon · 1×
Citations per year

Countries citing papers authored by Andreas Sackl

Since Specialization
Citations

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

Fields of papers citing papers by Andreas Sackl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Andreas Sackl, 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 Andreas Sackl Line = papers co-authored together Andreas Sackl links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201767
2 201659
3 201743
4 201240
5 201530
6 201827
7 201223
8 201322
9 201222
10 201321
11 201719
12 201218
13 201617
14 201516
15 201315
16 201413
17 202213
18 201412
19 201511
20 20149

About Andreas Sackl

Andreas Sackl is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications, Sociology and Political Science, Control and Systems Engineering and Marketing, having authored 43 papers that have together received 569 indexed citations. Recurring topics across this work include Image and Video Quality Assessment (26 papers), Multimedia Communication and Technology (7 papers), Caching and Content Delivery (6 papers), Network Traffic and Congestion Control (5 papers), Robot Manipulation and Learning (4 papers), E-Government and Public Services (3 papers), Consumer Market Behavior and Pricing (3 papers) and Visual Attention and Saliency Detection (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (372 citations), Human-Computer Interaction (60 citations), Media Technology (81 citations), Computer Networks and Communications (165 citations) and Signal Processing (75 citations). Andreas Sackl has collaborated with scholars based in Austria, Germany and United Kingdom. Frequent co-authors include Raimund Schatz, Sebastian Egger, Pedro Casas, Bruno Gardlo, Patrick Zwickl, Peter Reichl, Christian Timmerer, Peter Fröhlich, Manfred Tscheligi and Michael Seufert. Their work appears in journals such as IEEE Transactions on Network and Service Management, Lecture notes in computer science, Lecture notes in geoinformation and cartography, Information and Applied System Innovation.

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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