Title :
The least statistically-dependent basis and its applications
Author_Institution :
Dept. of Math., California Univ., Davis, CA, USA
Abstract :
Statistical independence is one of the most desirable properties of a coordinate system for representing and modeling images. In this paper we propose an algorithm to rapidly construct a coordinate system "closest" to the statistically independent one from a dictionary of bases such as the wavelet packets and local Fourier bases. The criterion is to minimize the sum of the coordinate-wise differential entropy and is quite different from the joint best basis (JBB) of Wickerhauser (1994). We demonstrate the use of the LSDB for image approximation and modeling, and compare its performance with Karhunen-Loeve basis (KLB) and JBB.
Keywords :
Fourier transforms; approximation theory; image representation; minimum entropy methods; statistical analysis; wavelet transforms; Karhunen-Loeve basis; algorithm; approximation; coordinate system; dictionary; differential entropy minimisation; image approximation; image modeling; image representation; joint best basis; least statistically-dependent basis; local Fourier bases; probabilistic modelling; statistical independence; wavelet packets; Character generation; Dictionaries; Face; Humans; Image coding; Image generation; Mathematics; Principal component analysis; Quantization; Wavelet packets;
Conference_Titel :
Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-7803-5148-7
DOI :
10.1109/ACSSC.1998.750958
