Joan Bartrina-Rapestà
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Media Technology top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Co-authors
- Joan Serra-SagristàFrancesc Aulí-LlinàsVíctor SánchezMichael W. MarcellinIan BlanesMiguel Hernández-CabroneroJuan Carlos MoureValero Laparra
- Topics
- Advanced Data Compression Techniques (50 papers)Image and Signal Denoising Methods (28 papers)Video Coding and Compression Technologies (11 papers)
- Partner nations
- SpainUnited StatesUnited Kingdom
In The Last Decade
Joan Bartrina-Rapestà
49 papers receiving 335 citations
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 309
- Signal Processing 120
- Media Technology 42
- Artificial Intelligence 31
- Computer Networks and Communications 13
Countries citing papers authored by Joan Bartrina-Rapestà
This map shows the geographic impact of Joan Bartrina-Rapestà'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 Joan Bartrina-Rapestà with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joan Bartrina-Rapestà more than expected).
Fields of papers citing papers by Joan Bartrina-Rapestà
This network shows the impact of papers produced by Joan Bartrina-Rapestà. 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 Joan Bartrina-Rapestà. The network helps show where Joan Bartrina-Rapestà may publish in the future.
Co-authorship network of co-authors of Joan Bartrina-Rapestà
This figure shows the co-authorship network connecting the top 25 collaborators of Joan Bartrina-Rapestà. A scholar is included among the top collaborators of Joan Bartrina-Rapestà 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 Joan Bartrina-Rapestà. Joan Bartrina-Rapestà is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 7 | |
| 9 | 12 | |
| 10 | 6 | |
| 11 | 5 | |
| 12 | 2 | |
| 13 | 14 | |
| 14 | 20 | |
| 15 | 31 | |
| 16 | 12 | |
| 17 | 6 | |
| 18 | 24 | |
| 19 | 1 | |
| 20 | 3 |
About Joan Bartrina-Rapestà
Joan Bartrina-Rapestà is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 54 papers that have together received 343 indexed citations. Recurring topics across this work include Advanced Data Compression Techniques (50 papers), Image and Signal Denoising Methods (28 papers) and Video Coding and Compression Technologies (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (309 citations), Signal Processing (120 citations) and Media Technology (42 citations). Joan Bartrina-Rapestà has collaborated with scholars based in Spain, United States and United Kingdom. Frequent co-authors include Joan Serra-Sagristà, Francesc Aulí-Llinàs, Víctor Sánchez, Michael W. Marcellin, Ian Blanes, Miguel Hernández-Cabronero, Juan Carlos Moure, Valero Laparra, Roberto Sarmiento and Johannes Ballé. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Access and Sensors.
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.