Rate distortion theory : a mathematical basis for data compression
Impact in
Classified as
- Authors
- Toby Berger
- Journal
- Prentice Hall eBooks
In The Last Decade
doi.org/w6835080 →Countries where authors are citing Rate distortion theory : a mathematical basis for data compression
This map shows the geographic impact of Rate distortion theory : a mathematical basis for data compression. 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 Rate distortion theory : a mathematical basis for data compression with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rate distortion theory : a mathematical basis for data compression more than expected).
Fields of papers citing Rate distortion theory : a mathematical basis for data compression
This network shows the impact of Rate distortion theory : a mathematical basis for data compression. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Rate distortion theory : a mathematical basis for data compression.
About Rate distortion theory : a mathematical basis for data compression
This paper, published in 1971, received 620 indexed citations . Written by Toby Berger covering the research area of Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Electrical and Electronic Engineering (280 citations), Computer Vision and Pattern Recognition (254 citations), Computer Networks and Communications (196 citations), Artificial Intelligence (164 citations) and Signal Processing (144 citations). Published in Prentice Hall 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/w6835080.