No-reference Image Quality Assessment using gradient-based Structural Integrity and Naturalness (Record no. 14685)
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000 -LEADER | |
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fixed length control field | 03632nam a22001697a 4500 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 616.075 |
Item number | K963N |
100 ## - MAIN ENTRY--AUTHOR NAME | |
Personal name | Kumar, Vineet |
245 ## - TITLE STATEMENT | |
Title | No-reference Image Quality Assessment using gradient-based Structural Integrity and Naturalness |
Statement of responsibility, etc | by Vineet Kumar |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication | IIT Jodhpur |
Name of publisher | Department of Electrical Engineering |
Year of publication | 2017 |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | ix,38p. |
Other physical details | HB |
520 ## - SUMMARY, ETC. | |
Summary, etc | "Human perception can distinguish between visual qualities of images through psycho-visual (subjective)<br/>assessment. However, to impart this quality to a machine is a challenging task, and therefore,<br/>objective image quality assessment plays a vital role in quantifying the visual quality of an image.<br/>Image quality assessment (IQA) aims at designing a mathematical model to gauge the overall<br/>perceptual quality of an image based on the characteristics that are consistent with the subjective<br/>evaluations. IQA plays an important role in various applications such as image compression,<br/>restoration, enhancement, and video streaming. IQA serves the purpose of monitoring image quality,<br/>benchmarking enhancement algorithms, and optimizing parameters in restoration algorithms.<br/>Early research, focused in the area of full-reference methods (where a reference image is<br/>available for comparison), presented a good understanding of characterizing visual quality in terms<br/>error sensitivity, structural similarity, contrast and luminance similarity. As a good reference image<br/>may not be available in most practical applications, this project focuses on no-reference image quality<br/>assessment (NR-IQA). This project proposes a no-reference image quality metric of naturalness<br/>based on the hypothesis that every image has latent additive white Gaussian noise (AWGN). The<br/>quality score consists of four contributing parameters: gradient-based structural integrity, contrast<br/>deviation, and Gabor-based smoothness and naturalness. The final score is a weighted summation<br/>of each of these individual quality factors. A mathematical model was developed on a dataset of<br/>about hundred test images by computing gradient-based structural similarity of corrupted images<br/>w.r.t. the original. Statistical modeling of the observations was found to fit an exponential parametric<br/>model. The standard deviation of the latent (or apparent) AWGN present in any image<br/>was estimated using an SVD-based approach. The factor of no-reference gradient-based structural<br/>integrity (NRGSI) is then computed by a simple back-projection of the estimated noise deviation<br/>on the exponential model. This serves as the first contributing factor. Other contributory factors<br/>are similarly defined based on naturalness, contrast and texture effects of degradation in an image.<br/>Mathematical modeling was performed using four hundred and fifteen natural/noisy images<br/>from standard databases. The proposed approach was further tested on a total of one hundred and<br/>sixty-two images. The overall performance of the proposed no-reference image quality metric is<br/>quantified by calculating the Spearman Rank Order Correlation Coefficient (SRCC) that denotes<br/>the accuracy of objective evaluation with respect to subjective (human visual) evaluation. The<br/>proposed IQA metric displays noteworthy performance (with an accuracy of 80% in terms of SRCC).<br/>In comparison with other state-of- the-art metrics, the proposed metric displays better, and in some<br/>cases, comparable performance.<br/>i"<br/> |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical Term | Image Quality Assessment |
Topical Term | MTech Theses |
Topical Term | Department of Electrical Engineering |
700 ## - ADDED ENTRY--PERSONAL NAME | |
Personal name | Chouhan, Rajlaxmi |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | Thesis |
Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Accession Number | Price effective from | Koha item type |
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Not For Loan | Reference | S. R. Ranganathan Learning Hub | S. R. Ranganathan Learning Hub | Course Reserve | 2024-01-18 | 616.075 K963N | TM00108 | 2024-01-18 | Thesis |