Title :
Differential encoding of high-resolution data
Author :
Poor, H. Vincent
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., NJ, USA
Abstract :
Predictive models for generating side information in differential encoding of data (e.g., images) sampled at high densities are considered. A model based on incremental difference operators is used in place of the conventional linear predictive coding (LPC) paradigm. It is seen that, unlike standard LPC, this formulation is stable for high sampling densities. Moreover, this formulation leads to more efficiently encoded side information in this regime
Keywords :
data compression; encoding; geophysical signal processing; geophysical techniques; image coding; remote sensing; differential encoding; geophysical measurement technique; high sampling densities; high-resolution; image coding; image compression; image processing; incremental difference operator; land surface; predictive model; remote sensing; side information; terrain mapping; Data mining; Discrete cosine transforms; Electronic mail; Encoding; Image coding; Image sampling; Linear predictive coding; Predictive models; Signal processing; Signal processing algorithms;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and Applications', International
Conference_Location :
Firenze
Print_ISBN :
0-7803-2567-2
DOI :
10.1109/IGARSS.1995.523993
