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Image Quality Assessment Project Thesis For Computer Theory and Engineering

Title: Weighted Structural Similarity Based on Edge Strength for Image Quality Assessment.
Department Of Computer Theory and Engineering
About Project: The role of images in present day communication has been steadily increasing. In this context the quality of an image plays a very important role. Different stages and multiple design choices at each stage exist in any image processing system. They have direct bearing on the quality of the resulting image. Unless we have a quantitative measure for the quality of an image, it becomes difficult to design an ideal image processing system. Though subjective quality assessment is an alternative, it is not feasible to be incorporated into real world systems. Hence, objective quality metrics play an important role in image quality assessment.
                        In this paper we present an image quality metric, which integrates the notions of structural similarity measure mimicking the overall functionality of HVS and visual regions of interest based on edge strength. We observed that the proposed index orrelates effectively with subjective scores and found to posses superior performance when compared with other metrics discussed in this paper. This paper is organized as follows. Section 2 explains the structural similarity method. Section 3 describes the computation of proposed quality index. Experimental results follow in Section 4. Finally, in Section 5, the conclusions of the paper are presented.
Abstract: In this paper a full reference objective image quality assessment technique is presented which is based on the properties of the human visual system (HVS). By integrating the notion of visual regions of interest with the measurement of structural similarity between the original image and distorted image a Weighted Structural Similarity Index (WSSI) is proposed. The method first evaluates the structural similarity indices between the original and distorted image in local regions. These local indices are then weighted based on the visual region of interest of the corresponding region, characterized by edge strength in the local region. WSSI of an image is calculated as the average of these weighted indices. A comparison with the peak-signal-to-noise ratio (PSNR) and state of the art metric, Mean Structural Similarity Index (MSSIM), shows that the proposed measure correlates better with the judgment of human observers.
Index Keywords: Edge strength, Human visual system, Structural similarity, Visual regions of interest.
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