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بسمه تعالی

بسمه تعالی. IQA Image Quality Assessment. 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

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بسمه تعالی

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  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-referencemage 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),

  12. Reduced-reference IQA (RRIQA) methods

  13. appropriate RR features are desirable to: • 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.

  14. 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,

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

  16. 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.

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

  18. 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. ParvezSazzad , 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.

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

  20. Let hf 0, hf 1, hf 2 and h0, h1, h2, respectively,bethe number of absolute difference amplitude pixelswith and without the edge preserving filter

  21. training-based approaches • only as reliable as the features used to train the learningmodel. • Algorithms following this approach often use a largenumber of features

  22. natural scene statistics (NSS) • relies on extensivestatistical modeling • reliable generalization of visualcontent and the perception of it.

  23. Blind Image Quality Assessment: From NaturalScene Statistics to Perceptual Quality”Anush Krishna Moorthy, Alan Conrad Bovik,“ • wavelet-based algorithm • combination of the second andthe 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.

  24. Some NSS methods: • BLIINDS • BRISQUE • LBIQ • Natural Image Quality Evaluator (NIQE)

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