Representative vector of each class is beforehand set at the codebook. To begin with, the sampled-data of m piece of the input image comes in for the input buffer and is memorized. This input vector and representative vector of each class in the codebook are compared in order, and belonging to class is decided. Then, the number of representative vector of the class is decided, and it is output as an encoding output. It decides the cluster which the input vector f k belongs to fJ by using following equation.
E=| f ^k - f _j | ²            (2.1)
Square distance E between vectors is calculated, and it is decided in the result of minimizing this. In the decoding side, the representative vector is read out from the decoding signal using the codebook of the content equal to the encoding side, and the decoding block is output.

The preparation of the codebook for the vector quantization of the innerband pixel block
The algorithm in making the codebook is shown in the following.

1. The vector is early uniformly placed in the past 16th order space.
2. The input of the original picture is assigned in the class of the closest representative vector.
3. The new representative vector is made in the mean value in each class.
4. It is halved from the class of which the dispersion is the biggest, when representative vector which is not used produced.
5. The work of 2 ~ 4 is repeated

The vector quantization of the interband identity position pixel block.
The vector quantization of interband identity position pixel block which does vector quantization using features of multiband satellite image is proposed, because vector quantization is done for multiband satellite image. It is unique for the spectral reflectance of the object, and it is different by the type of the object. Then, in making the every pixel to be a unit using this, the value of each band is made to be a vector. Though for the image of 512 X 512 pixel of 7 bands, 262,144 vectors are formed as 7^th order vector, this vector is represented at 256 vectors. The mean value of each every item was utilized using training data used in the land cover classification as a vector this time early. By making the codebook from the vector in initial stage, the high quality image is more obtained. The algorithm in making the codebook for vector quantization of encoding algorithm of interband identity position pixel is shown in the following.

1. The vector is early placed on the basis of training data used in the past seventh space in land cover and classification, etc..
2. The input of the original picture is assigned in the class of the closest representative vector.
3. The new representative vector is made in the mean value in each class.
4. It is halved from the class of which the dispersion is the biggest, when representative vector which is not used produced.
5. The work of 2 ~ 4 is repeated.

Experiment
Vector quantization of the innerband pixel block and vector quantization of the interband identity position pixel block are done, and the result is shown. And, the result of the compression encoding using wavelet transform is shown as comparison object with that vector quantization.

Used satellite image data
The image with the object in this experiment utilized the data which consisted of 7 bands of Landsat/TM. This images are Kanazawa City in August, 1984 and the nearby region, and they consist of each 1 pixel 8bit, 512 X 512 pixel, and the size of the image is 1835,008(byte). And, the equal image in August, 1985 and November, 1991 of the range it confirms the effectiveness of the vector early is used for the experiment.