论文信息 - Image Compression Using SVD Technique and Measurement of Quality Parameters

Image Compression Using SVD Technique and Measurement of Quality Parameters

Singular value decomposition (SVD) based algorithm is designed to compress the black and white images to evaluate the effect of information content of the image. Inorder to evaluate the effect second algorithm is developed to calculate the SNR, PSNR, MSE, and RMSE value of the compressed images with respect to reference image. It is interpreted from the results that high level of compression the information content of the image is degraded to higher level.

[1] Vps Naidu. Discrete Cosine Transform-based Image Fusion , 2010 .

[2] H. Andrews,et al. Singular Value Decomposition (SVD) Image Coding , 1976, IEEE Trans. Commun..

[3] Gonzalo R. Arce,et al. Nonlinear Signal Processing - A Statistical Approach , 2004 .

[4] Michael W. Marcellin,et al. JPEG2000 - image compression fundamentals, standards and practice , 2013, The Kluwer international series in engineering and computer science.

[5] W. J. Dowling,et al. Image coding using the singular value decomposition and vector quantization , 1995 .

[6] Gene H. Golub,et al. Matrix computations , 1983 .

[7] Jar-Ferr Yang,et al. Combined techniques of singular value decomposition and vector quantization for image coding , 1995, IEEE Trans. Image Process..

[8] Jan J. Gerbrands,et al. On the relationships between SVD, KLT and PCA , 1981, Pattern Recognit..

[9] Michael Elad,et al. Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..

[10] Rémi Vaillancourt,et al. Image compression with multiresolution singular value decomposition and other methods , 2005, Math. Comput. Model..