Abstract:

This paper presents a new coding technique based on contourlet transform and multistage vector quantization. Wavelet based Algorithms for image compression results in high compression ratios compared to other compression techniques. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently take advantage of the fact that the edges usually found in natural images are smooth curves. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. The computation and storage requirements are the major difficulty in implementing a vector quantizer. In the full-search algorithm, the computation and storage complexity is an exponential function of the number of bits used in quantizing each frame of spectral information. The storage requirement in multistage vector quantization is less when compared to full search vector quantization. The coefficients of
contourlet transform are quantized by multistage vector quantization. The quantized coefficients are encoded by Huffman coding to get better quality i.e., high peak signal to noise ratio (PSNR). The results obtained are tabulated and compared with the existing wavelet based ones.

Keywords: Contourlet Transform, Directional Filter bank, Laplacian Pyramid, Multistage Vector Quantization.

BibTex:

@ARTICLE{P1150627001,

AUTHOR = {S.Esakkirajan and T.Veerakumar andV. Senthil Murugan and R.Sudhakar},

TITLE = {IMAGE COMPRESSION USING CONTOURLET TRANSFORM AND MULTISTAGE VECTOR QUANTIZATION},

JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing},

}

(Full Paper 1,196KB)