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Patrick J. Ready

dblp:273/1701 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1973
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video coding · 56% Image and video processing · 44%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding › image compression
multispectral image compression		 0.011973
Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery · IEEE Trans. Commun. 1973
Image and video processing › remote sensing › remote sensing image processing
multispectral image processing		 0.011973
Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery · IEEE Trans. Commun. 1973
Image and video coding
transform coding		 0.011973
Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery · IEEE Trans. Commun. 1973
Image and video processing
image enhancement		 0.011973
Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery · IEEE Trans. Commun. 1973
Image and video processing
signal-to-noise ratio improvement		 0.011973
Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery · IEEE Trans. Commun. 1973

Methods — techniques the papers use, named apart from their topics

principal component analysis · 0.0karhunen-loève transformation · 0.0
YearPublicationVenuePosition
1973 Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery
abstract
The Karhunen-Loève transformation is applied to multispectral data for information extraction, SNR improvement, and data compression. When applied in the spectral dimension, the transform provides a set of uncorrelated principal component images very useful in automatic classification and human interpretation. Significant improvements in SNR and estimates of the noise variance are also shown to be possible in the spectral dimension. Data compression results using the transform on one-, two-, and three-dimensional blocks over three general types of terrain are presented.
Patrick J. Ready, Paul A. Wintz
 IEEE Trans. Commun.1