This paper presents a novel approach to design a vector quantizer for image compression. Compression of image data using Vector Quantization (VQ) will compare Training Vectors with Codebook that has been designed. The result is an index of position with minimum distortion. Moreover it provides a means of decomposition of the signal in an approach which takes the improvement of inter and intra band correlation as more lithe partition for higher dimension vector spaces. Thus, the image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. In addition the proposed paper also presents a survey on different methods of vector quantization for image compression and application of SOFM.
Lenka, Rasmita; Padhi, Swagatika; Behera, Minakshee; Patnaik, Naresh; and Mohanty, Mihir N.
"DESIGN OF NEURO-WAVELET BASED VECTOR QUANTIZER FOR IMAGE COMPRESSION,"
International Journal of Computer and Communication Technology: Vol. 2
, Article 10.
Available at: https://www.interscience.in/ijcct/vol2/iss1/10