Sattelite Image Data Compression using Vector Quantization on Wavelet Information
S. Wongkharn, M. Chongcheawchamnan
Faculty of Engineering mahanakorn University of Technogogy
Bangkok 10530, Thailand
K. Kittayaruasiriwat, F. Cheevasuvit, K.Dejhan
Faculty of Engineering, King Mongkut's Institute of Tecnology ladkraband
Bangkok 10520, Thailand
Abstract
The application of wavelet transform on an image results subband images. These subband images compose of a low-pass subband image (approximation image) and three high-pass subband images (detail images). We can apply the vector quantization (VQ) on these high pass subband images to compress image data. But this technique loses some edge's details. Since the edges of image are important for the sattelite image processing such as an image interpretation and an image classification. Therefore, this paper presents a settelite image data compression technique using vector quantization on wavelet subband image by maintaining the edge's details. The proposed technique gives more gradient informations which relate to the information details of the edges.
(1) Wavelet Transform
Wavelet transform (WT) in the image processing can be considered as a subband decomposition [1],[2],[3]. Fig. 1(a) shows an image wavelet decomposition diagram. The original image fL (x,y) is firstly filtered on the row by applying filter H and G and downsampled by keeping one column out of two. Two resulting images, the low-pass fL (x,y) and high-pass fH (x,y)outputs are obtained. Then, both of them are filtered along the column and upsampled by keeping one row out of two. It can be obtained one low-pass subband image denoted by fLL (x,y) and three high-pass subband images denoted by fLH (x,y), fHL (x,y) and fHH (x,y, respectively. The image fLL (x,y) is a dc component while the other threes are image's edge information. Finally, the image wavelet reconstruction is show in Fig. 1 (b)
(2) Vector quantization
Vector quantization (VQ) [4] is a lossy compression technique. This technique is composed of two part, encoder and decoder and decoder. An encoder will compare each input vector with every code vector in the codebook and generate index which represent the minimum distortion code vector from the input vector. A decoder takes the indexs to locate the codevector in the codebook and generate the output vectors.
A codebook is the set of finite codevector for representing the input vectors. The popular technique in codebook design is the Linde-Buzo-Gray (LBG) algorithm [5],[6].
(a) image wavelet Decomposition
(b) Image Wavelet Reconstructed
Fig.1 Image Wavelet Transform and Its Inverse
