Francesco Rizzo
- Computer Vision and Pattern Recognition top 5%
- Media Technology top 5%
- Artificial Intelligence
- Computational Mechanics
- Signal Processing
- Co-authors
- James A. StorerGiovanni MottaBruno CarpentieriGaetano MottaMaurizio ColluMichael BorgAndrew Shires
- Topics
- Advanced Data Compression Techniques (15 papers)Image and Signal Denoising Methods (6 papers)Algorithms and Data Compression (6 papers)
- Journals
- Information SciencesIEEE Signal Processing LettersKluwer Academic Publishers eBooks
- Partner nations
- United StatesItalyUnited Kingdom
In The Last Decade
Francesco Rizzo
15 papers receiving 333 citations
Peers
Comparison fields: 5 of 40
- Computer Vision and Pattern Recognition 299
- Media Technology 91
- Artificial Intelligence 71
- Computational Mechanics 43
- Signal Processing 40
Countries citing papers authored by Francesco Rizzo
This map shows the geographic impact of Francesco Rizzo'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 Francesco Rizzo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Rizzo more than expected).
Fields of papers citing papers by Francesco Rizzo
This network shows the impact of papers produced by Francesco Rizzo. 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 Francesco Rizzo. The network helps show where Francesco Rizzo may publish in the future.
Co-authorship network of co-authors of Francesco Rizzo
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Rizzo. A scholar is included among the top collaborators of Francesco Rizzo 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 Francesco Rizzo. Francesco Rizzo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 21 | |
| 2 | 117 | |
| 3 | 86 | |
| 4 | Compression of Hyperspectral Imagery. | 15 |
| 5 | 12 | |
| 6 | 4 | |
| 7 | 5 | |
| 8 | 16 | |
| 9 | 43 | |
| 10 | 6 | |
| 11 | Adaptive vector quantization for images and hyperspectral data | 0 |
| 12 | 0 | |
| 13 | 3 | |
| 14 | Image compression | 2 |
| 15 | 18 | |
| 16 | 2 | |
| 17 | 1 |
About Francesco Rizzo
Francesco Rizzo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 17 papers that have together received 351 indexed citations. Recurring topics across this work include Advanced Data Compression Techniques (15 papers), Image and Signal Denoising Methods (6 papers) and Algorithms and Data Compression (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (299 citations), Media Technology (91 citations) and Computational Mathematics (4 citations). Francesco Rizzo has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include James A. Storer, Giovanni Motta, Bruno Carpentieri, Gaetano Motta, Maurizio Collu, Michael Borg and Andrew Shires. Their work appears in journals such as Information Sciences, IEEE Signal Processing Letters and Kluwer Academic Publishers eBooks.
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.