Lossy compression algorithm of remotely sensed multispectral images based on YCrCb transform and IWT

2007 ◽  
Author(s):  
Bao-feng Tian ◽  
Jun Lin ◽  
Xin Wang
Keyword(s):  
Remotely Sensed ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Multispectral Images

scholarly journals Temporal change enhancement in multispectral images remotely sensed from satellites

1997 ◽  
Author(s):  
Bill P. Pfaff ◽  
Doran Baker ◽  
Lloyd G. Allred ◽  
Gene Ware
Keyword(s):  
Temporal Change ◽  
Remotely Sensed ◽  
Multispectral Images

Lossy compression of Landsat multispectral images

2016 ◽  
Author(s):  
Ruslan Kozhemiakin ◽  
Sergey Abramov ◽  
Vladimir Lukin ◽  
Blazo Djurovic ◽  
Igor Djurovic ◽  
...  
Keyword(s):  
Lossy Compression ◽  
Multispectral Images

scholarly journals Context Based Predictive Information

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 645
Author(s):  
Yuval Shalev ◽  
Irad Ben-Gal
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Predictive Information ◽  
Real Dataset ◽  
Information Bottleneck ◽  
The U.S

We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks’ stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version.

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