Keyword: Codebook, Landsat TM, Innerband, Interband

Abstract:
Block transformation encoding system using the DCT(discrete cosine transform) has been standardized by JPEG (Joint Photographic Experts Group), and there is a problem of remarkably recognizing the block strain at the low rate. The application of wavelet transform to the image coding was started using analysis (Multi-Resolution Analysis :MRA) degree of the multiple solution images by Mallet in 1988. The picture quality for the sub-band code system using wavelet transform at the low rate is high. The data volume seems to become enormous by high resolution of the image with performance enhancement of the sensor and increase in the band number for the data got by the sensor of satellite. By it, there is the necessity of compressing the data. In this study, the image was divided into blocks for multiband image of the 7 bands got by Landsat TM, and the block was made to be a unit, and vector quantization was tried every each block. And, the spectral reflectance of the object is different by the type of the object, and the spectral reflection from the object proposes the method for using the vector quantization using distinguishing the object by the spectral reflection, every pixel, since the effect of the spectral reflectance is being received. The lossy encoding technique using this vector quantization of the every block and, lossy encoding technique which used wavelet transform for vector quantization of the every pixel and predictive coding is compared.

Introduction
The research on the encoding of the image has the history over 30, and by extending present, the research is actively carried out more and more. This depends on the development of research environments. The representative equipment is a computer, and it is also the speedup complicated encoding.

In the encoding of the still picture, because the effect which reduces the redundancy of information is high, the bandwidth compression using DCT has been standardized by JPEG, and there is a problem of remarkably recognizing the block strain at the low rate DCT block transformation encoding system. The application of the wave let conversion to the image coding was started using the analysis degree of multiple solution images by Mallet in 1988. For the block transformation encoding using the DCT, the picture quality for sub-band code system using the wave let conversion at the low rate is high-grade. The data volume seems to become enormous by high resolution of the image with performance enhancement of the sensor and increase in the band number for the data got by the sensor of satellite. By it, it is necessary to compress the data, when the burden of the transfer from satellite and satellite in the receiving station memory capacity. In this study, the image was divided for multiband image of the 7 bands got by Landsat TM in large number of blocks, and the block was made to be a unit, and vector quantization was tried every each block. And, the method for using the vector quantization using distinguishing the object by the spectral reflection is proposed.

Vector Quantization
The whole image region is divided into the block of n X n, and the method for expressing gray level of all pixels in this block unit at 1 vector is the vector quantization. Vector quantization is efficient compression method of image and system for noticing in pattern recognition. The algorithm is shown in the following.

Vector quantization algorithm of the innerband pixel block
The encoding is done in the m dimension Euclidean space, when the picture element number in the block was made to be m = n X n. That is to say, the picture signal of one block seems to be a vector of m dimension with the component of the m piece, and it forms 1 vector in the m dimensional space. With that the vector is obtained for all screens of the image of 1 band given this time, the class with approached vector value is formed. Then, it is possible that it selects representative vector of each class and approximates it by the representative vector, when the optional input vector was given. For example, the vector of the 16,384 is formed in the whole screen, when 4 X 4 arranging block for the image of the 512 X 512 pixel is made to be vector 16^th order vector. The data compression is realized by conducting the clustering for this vector group, and representing at 256 vectors. Encoding algorithm of the vector quantization of innerband pixel block is shown in Fig. 2.1.


Fig. 2.1 Encoding algorithm of the vector quantization of innerband pixel block