Image Quality Assessment with Structural Similarity Using Wavelet Families at Various Decompositions

Wavelet transform is one of the most active areas of research in the image processing. This paper gives analysis of a very well known objective image quality metric, so called Structural similarity, MSE and PSNR which measures visual quality between two images. This paper presents the joint scheme of wavelet transform with structural similarity for evaluating the quality of image automatically. In the first part of algorithm, each distorted as well as original image are decomposed into three levels and in second part, these coefficient are used to calculate the structural similarity index, MSE and PSNR. The predictive performance of image quality based on the wavelet families like db5, haar (db1), coif1 with one, two and three level of decomposition is figured out. The algorithm performance includes the correlation measurement like Pearson, Kendall, and Spearman correlation between the objective evaluations with subjective one.