The visible differences predictor: an algorithm for the assessment of image fidelity
- Authors
- Scott Daly
- Journal
- MIT Press eBooks
In The Last Decade
doi.org/w5221113 →Countries where authors are citing The visible differences predictor: an algorithm for the assessment of image fidelity
This map shows the geographic impact of The visible differences predictor: an algorithm for the assessment of image fidelity. 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 The visible differences predictor: an algorithm for the assessment of image fidelity with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The visible differences predictor: an algorithm for the assessment of image fidelity more than expected).
Fields of papers citing The visible differences predictor: an algorithm for the assessment of image fidelity
This network shows the impact of The visible differences predictor: an algorithm for the assessment of image fidelity. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The visible differences predictor: an algorithm for the assessment of image fidelity.
About The visible differences predictor: an algorithm for the assessment of image fidelity
This paper, published in 1993, received 597 indexed citations . Written by Scott Daly. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (539 citations), Atomic and Molecular Physics, and Optics (155 citations), Media Technology (141 citations), Computer Graphics and Computer-Aided Design (116 citations) and Cognitive Neuroscience (112 citations). Published in MIT Press 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.
This paper is also available at doi.org/w5221113.