International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Publisher

IJIGSP Vol.6, No.2, Jan. 2014

Wavelet Transform Techniques for Image Compression – An Evaluation

Full Text (PDF, 1205KB), PP.54-67

Views:1   Downloads:0


S. Sridhar,P. Rajesh Kumar,K.V.Ramanaiah

Index Terms



A vital problem in evaluating the picture quality of an image compression system is the difficulty in describing the amount of degradation in reconstructed image, Wavelet transforms are set of mathematical functions that have established their viability in image compression applications owing to the computational simplicity that comes in the form of filter bank implementation. The choice of wavelet family depends on the application and the content of image. Proposed work is carried out by the application of different hand designed wavelet families like Haar, Daubechies, Biorthogonal, Coiflets and Symlets etc on a variety of bench mark images. Selected benchmark images of choice are decomposed twice using appropriate family of wavelets to produce the approximation and detail coefficients. The highly accurate approximation coefficients so produced are further quantized and later Huffman encoded to eliminate the psychovisual and coding redundancies. However the less accurate detailed coefficients are neglected. In this paper the relative merits of different Wavelet transform techniques are evaluated using objective fidelity measures- PSNR and MSE, results obtained provide a basis for application developers to choose the right family of wavelet for image compression matching their application.

Cite This Paper

S. Sridhar,P. Rajesh Kumar,K.V.Ramanaiah,"Wavelet Transform Techniques for Image Compression – An Evaluation", IJIGSP, vol.6, no.2, pp.54-67, 2014.DOI: 10.5815/ijigsp.2014.02.07


[1]Vetterli, Kovvcevic, 1995. Wavelets and subband coding.

[2]B.Eswara Reddy and K.Venkata Narayana, “A lossless image compression using traditional and lifting based wavelets”.

[3]Yogendra Kumar Jain and Sanjeev Jain, “Performance Evaluation of Wavelets for Image Compression”.

[4]Faisal Zubir Quereshi, “Image Compression using Wavelet Transform”.

[5]Kareen Lees, “Image compression using wavelets”.

[6]S.Suresh Kumar and H.Mangalam, “Wavelet Based Image Compression of Quasi-Encrypted Grayscale Images”. 

[7]Priyanka Singh, Priti Singh,” JPEG Image Compression based on Biorthogonal, coiflets and Daubechies Wavelets”.

[8]Mahesh S. Chavan, Nikos Mastorakis, Manjusha N. Chavan,” Implementation of SYMLET Wavelets to Removal of Gaussian Additive Noise from Speech Signal”.

[9]Mohammed A. Salem, Nivin Ghamry, and Beate Meffert, “Daubechies versus Biorthogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems”.

[10]Gerlind Plonka, Hagen Schumacher and Manfred Tasche, “Numerical stability of biorthogonal wavelet transforms”.

[11]Michail Shnaider, Andrew P Paplinski, “Wavelet transform in image coding”.

[12]Sonal and Dinesh Kumar, “A study of various Image Compression Techniques”, Guru Jhmbheswar university of science and technology, Hisar.

[13]Chun-Lin, Liu, “A tutorial of the Wavelet Transform”.

[14]Marta Mrak and Sonia Grgic, “Picture quality Measures in Image Compression Systems”, EUROCON 2003 Ljubljana, Slovenia.