A SYSTEMATIC IMAGE COMPRESSION IN THE COMBINATION OF LINEAR VECTOR QUANTISATION AND DISCRETE WAVELET TRANSFORM

K. Kalaivani, C. Thirumaraiselvi

Abstract: As the use of digital image is increasing day by day, and the amount of data required for an acceptable quality image is high, there begins a high necessity for image compression. Vector quantisation (VQ) is a novel technique for image compression. VQ is a lossy compression scheme, used to compress image both in spatial domain & frequency domain. One of the major disadvantages is high encoding time & complexity. In spite of these disadvantages it is highly preferred due to its advantages like high reconstruction quality at low coding rates and rapid decoding. Thus in order to reduce the high encoding time we go for the use of neural network. There are various types of neural networks are available. The proposed algorithm uses the most effective and simple methods like self organizing maps and linear vector quantization together with the discrete wavelet transform in order to reduce the loss of information during compression and their results are compared.

Keywords: Image compression, Neural network, Self organising maps, Linear vector quantization, discrete wavelet transform.

DOI: https://doi.org/10.15623/ijret.2014.0304044