3.Vector Quantization
Image compression using vector quantization (VQ) is a powerful lossy compression technique. The V1 algorithm is tried to create a codebook that is a set by finite code vectors for representing the input vector. These code vectors are generated under the constraint of minimum distortion. The most well-known technique of codebook design is presented by Linde-Buzo-Gray [3]. Since the 2²⁴ colour shades of PCA colour image will be compressed into 256 colours. Therefore, the initial 256 code vectors must be selected form most frequency appear of 256 colour from the PCA colour image via the 3-dimensional cluster diagram. Consequently, the computation time in code vector training process will be diminished. These 256 code vectors will be kept in the codebook. VQ encoder will compare will compare each colour of PCA image will all the 256 code vectors in order to generate in index under the minimum distortion criterion. By the decoder side, the index in used to point out the location of corresponding colour in the codebook. The output vector from the decoder will be the approximately colour of the corresponding pixel. The encoder and decoder of VQ can be presented as the Fig. 4.

(a) Transmitter



(b) Receiver
Fig 4. Block diagram of VQ process
By applying the VQ method that defined initial code vectors to the Fig. 3, compressed image is shown in Fig 5(a) with the mean square error of 6.52. While the Fig. 59b) is the resulting image using random initial code vectors, and its mean square error is of 129.64.

(a) Defined initial code vectors



(b) Random initiall code vectors
Fig.5 Compressed PCA image with the compression ratio 3:1.
4. Conclusion
The PCA has been shown as a powerful method for multispectral images interpretation especially in the colour composite form. However, each colour image needs a huge of memory storage and channel bandwidth. Therefore, this paper presents a vector quantization method to compress the PCA colour image. The initial code vector in the vector quantization's codebook are defined by the most frequently appear colours in the PCA image in order to reduce the computation time and also the mean square error. The compressed colour image will be suitable to transmit to the remote organization via the public communication system. Hence, the end users can be widely utilized the compressed PCA colour image for their objectives.

References

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• Y.Linde, A. Buzo and R.M. Gray, " An algorithm for vector quatizatizer design, "IEEE Trans. commun, vol. COM-28, pp. 84-95, Jan 1980.
• F.Cheevasuvit, K. Dejhan, S.Mitatha and S.Wongkharn, "Data compression using vector quantization and Huffman coding for satellite imagery," Proc. of the 16^th CRS, pp. E1-1E1-5,1995.