The assessment of image quality is very important for numerous image processing applications, where the goal of
image quality assessment (IQA) algorithms is to automatically assess the quality of images in a manner that is consistent
with human visual judgment. Two prominent examples, the Structural Similarity Image Metric (SSIM) and Multi-scale
Structural Similarity (MS-SSIM) operate under the assumption that human visual perception is highly adapted for
extracting structural information from a scene. Results in large human studies have shown that these quality indices
perform very well relative to other methods. However, the performance of SSIM and other IQA algorithms are less
effective when used to rate amongst blurred and noisy images. We address this defect by considering a three-component
image model, leading to the development of modified versions of SSIM and MS-SSIM, which we call three component
SSIM (3-SSIM) and three component MS-SSIM (3-MS-SSIM).
A three-component image model was proposed by Ran and Farvardin,  wherein an image was decomposed into
edges, textures and smooth regions. Different image regions have different importance for vision perception, thus, we
apply different weights to the SSIM scores according to the region where it is calculated. Thus, four steps are executed:
(1) Calculate the SSIM (or MS-SSIM) map. (2) Segment the original (reference) image into three categories of regions
(edges, textures and smooth regions). Edge regions are found where a gradient magnitude estimate is large, while smooth
regions are determined where the gradient magnitude estimate is small. Textured regions are taken to fall between these
two thresholds. (3) Apply non-uniform weights to the SSIM (or MS-SSIM) values over the three regions. The weight for
edge regions was fixed at 0.5, for textured regions it was fixed at 0.25, and at 0.25 for smooth regions. (4) Pool the
weighted SSIM (or MS-SSIM) values, typically by taking their weighted average, thus defining a single quality index for
the image (3-SSIM or 3-MS-SSIM).
Our experimental results show that 3-SSIM (or 3-MS-SSIM) provide results consistent with human subjectivity
when finding the quality of blurred and noisy images, and also deliver better performance than SSIM (and MS-SSIM) on
five types of distorted images from the LIVE Image Quality Assessment Database.