000			03632nam a22001697a 4500
082			_a616.075 _bK963N
100			_aKumar, Vineet _926084
245			_aNo-reference Image Quality Assessment using gradient-based Structural Integrity and Naturalness _cby Vineet Kumar
260			_aIIT Jodhpur _bDepartment of Electrical Engineering _c2017
300			_aix,38p. _bHB
520			_a"Human perception can distinguish between visual qualities of images through psycho-visual (subjective) assessment. However, to impart this quality to a machine is a challenging task, and therefore, objective image quality assessment plays a vital role in quantifying the visual quality of an image. Image quality assessment (IQA) aims at designing a mathematical model to gauge the overall perceptual quality of an image based on the characteristics that are consistent with the subjective evaluations. IQA plays an important role in various applications such as image compression, restoration, enhancement, and video streaming. IQA serves the purpose of monitoring image quality, benchmarking enhancement algorithms, and optimizing parameters in restoration algorithms. Early research, focused in the area of full-reference methods (where a reference image is available for comparison), presented a good understanding of characterizing visual quality in terms error sensitivity, structural similarity, contrast and luminance similarity. As a good reference image may not be available in most practical applications, this project focuses on no-reference image quality assessment (NR-IQA). This project proposes a no-reference image quality metric of naturalness based on the hypothesis that every image has latent additive white Gaussian noise (AWGN). The quality score consists of four contributing parameters: gradient-based structural integrity, contrast deviation, and Gabor-based smoothness and naturalness. The final score is a weighted summation of each of these individual quality factors. A mathematical model was developed on a dataset of about hundred test images by computing gradient-based structural similarity of corrupted images w.r.t. the original. Statistical modeling of the observations was found to fit an exponential parametric model. The standard deviation of the latent (or apparent) AWGN present in any image was estimated using an SVD-based approach. The factor of no-reference gradient-based structural integrity (NRGSI) is then computed by a simple back-projection of the estimated noise deviation on the exponential model. This serves as the first contributing factor. Other contributory factors are similarly defined based on naturalness, contrast and texture effects of degradation in an image. Mathematical modeling was performed using four hundred and fifteen natural/noisy images from standard databases. The proposed approach was further tested on a total of one hundred and sixty-two images. The overall performance of the proposed no-reference image quality metric is quantified by calculating the Spearman Rank Order Correlation Coefficient (SRCC) that denotes the accuracy of objective evaluation with respect to subjective (human visual) evaluation. The proposed IQA metric displays noteworthy performance (with an accuracy of 80% in terms of SRCC). In comparison with other state-of- the-art metrics, the proposed metric displays better, and in some cases, comparable performance. i"
650			_aImage Quality Assessment _926085
650			_aMTech Theses _926086
650			_aDepartment of Electrical Engineering _926087
700			_aChouhan, Rajlaxmi _926088
942			_cTH
999			_c14685 _d14685