Matching images using Mean Squared Error (MSE) and Peak Signal to Noise (PSNR) ratios does not well conform to the Human Visual System (HVS). When matching two images, HVS operates both globally and locally when it identifies features of a scenery and this process is not matched adequately by PSNR or MSE. A low MSE or very high PSNR may not necessarily mean that images are similar. Similarly, when images are similar as HVS would identify, the corresponding MSE may not be very low and PSNR may not be very high. However, quite recently, a new measure has been proposed to circumvent the drawbacks of PSNR or MSE. This measure known as Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite SSIM s popularity, it does not provide adequate insight into how it handles structural similarity of images. We propose a new structural similarity measure based on approximation level of a given discrete Wavelet decomposition that evaluates moment invariants to capture the structural similarity with superior results over SSIM.