1. بسمه تعالی IQA Image Quality Assessment

2. Introduction Goal : develop quantitative measures that can automatically predict perceived image quality. 1-can be used to dynamically monitor 2-can be used to optimize algorithms and parameter settings 3-can be used to benchmark image processing systems and algorithms

3. application

4. Different methods: 1- subjective:Mean Opinion Score (MOS) 2- objective

5. Objective image quality metrics full-reference, reduced-reference no-reference or “blind”

6. full-reference mage quality assessment. mean squared error (MSE) noise quality measure (NQM) universal quality index (UQI) Peak signal-to-noise ratio (PSNR) The structural similarity (SSIM) the multi-scale SSIM (MS-SSIM) the information fidelity criterion(IFC) visual signal to noise ratio (VSNR)

7. SNR & PSNR

8. Structural Similarity Based Image Quality Assessment The system separates the task of similarity measurement into three comparisons: -luminance -contrast -structure.

9. Diagram of the structural similarity (SSIM) measurement

10. luminance of each signal Standard deviation: Third, the signal is normalized:

11. we need to define the three functions: l(x; y), c(x; y), s(x; y) where L is the dynamic range of the pixel values (255 for 8-bit grayscale images),

13. Reduced-reference IQA (RRIQA) methods

14. appropriate RR features are desirable to: 1)provide an efficient summary of the reference image; 2) be sensitive to a variety of image distortions; 3) be relevant to the visual perception of image quality.

15. Three different but related types of approaches have been employed: 1- modeling image distortions are mostly developed for specific application environments 2-modeling the human visual system Perceptual features motivated from computational models of low level vision were extracted 3- modeling natural image statistics most real-world image distortions disturb image statistics and make the distorted image “unnatural.” it does not require any training, and has a rather low RR data rate,

16. modeling image distortions Reduced-reference picture quality estimation by using local harmonic amplitude information (local harmonic amplitude information computed from an edge-detected picture)

17. modeling the human visual system An image quality assessment method based on perception of structural information Perceptual Representation: -set of low level processings - perceptual subband decomposition.

19. no-reference or “blind” absence of a reference without assuming a single distortion type NR-IQA algorithms generally follow one of three trends: 1)distortion-specific approaches. 2) training-based approaches. 3) natural scene statistics (NSS)

20. distortion-specific approaches These algorithms quantify one or more distortions such as blockiness, blur or ringing Example: No reference image quality assessment for JPEG2000 based on spatial features “Z.M. Parvez Sazzad, Y. Kawayoke, Y. Horita” natural image signals are highly structured signals have strong dependencies between each other, any kind of artifacts creates pixel distortions from neighborhood pixels.

23. Let X be the central pixel and Q1;Q2;... ;Q16 the second closest neighborhood

24. Let hf 0, hf 1, hf 2 and h0, h1, h2, respectively,be the number of absolute difference amplitude pixels with and without the edge preserving filter

25. training-based approaches only as reliable as the features used to train the learning model. Algorithms following this approach often use a large number of features

26. natural scene statistics (NSS) relies on extensive statistical modeling reliable generalization of visual content and the perception of it.

27. Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality ” Anush Krishna Moorthy, Alan Conrad Bovik,“ wavelet-based algorithm combination of the second and the third approaches It uses a two-stage framework, 1) support vector machine (SVM) to classify an image into a distortion class 2) support vector regression to predict quality scores.

28. Some NSS methods: 1.BLIINDS 2.BRISQUE 3.LBIQ 4.Natural Image Quality Evaluator (NIQE)