Comparative Study of Vector Quantization in Image Compression

Image Compression holds a technique of compressing the data which encodes the standard image with fewer amount of bits, which
results from particular sampling values using quantization methods. Quantization is a lossy compression technique, which is done by
minimizing a set of bits into single quantum bit. In Vector Quantization, codebook is the significant part. The various techniques for
quantization are explained in this paper. Some popular quantization techniques consume less memory and computation time such as
LBG80, FBP, BPNN, ENN and Simple nearest neighbor algorithm. The resultant values of different quantization are explained in an
experimental part, with the use of standard performance measure of MSE and PSNR. This approach gives an effective Codebook with
minimum computational time and includes excellent Peak Signal to Noise Ratio values (PSNR).