A Review in Quality Measures for Halftoned Images

1. A Review in Quality Measuresfor Halftoned Images Student Per-Erik Axelson

2. Image Quality • Subjective Quality • Subjective test (MOS): Best way so far to assess and judge image quality • This method is to inconvenient, slow and expensive for practical usage • Objective Quality Measures • The goal of objective image quality assessment research is to supply quality metrics that can predict perceived image quality automatically Ph.D. Course in Digital Halftoning

3. Image quality paradigm Objective Image Quality Original Image Reproduced Image • Most important demand • In General, we want binary image to match continuous-toneoriginal as closely as possible Ph.D. Course in Digital Halftoning

4. Objective Image Quality • A number of demands • The method should be able to evaluate all kinds of halftones and provide a meaningful comparison across different types of image distortions • The method should return measures for several aspects of quality that are well correlated with results from subjective tests • The method should be easy to calculate and have low computational complexity Ph.D. Course in Digital Halftoning

5. Objective Image Quality • Useful applications • Be used to monitor image quality for quality control systems • Be employed to benchmark halftoning algorithms • Be embedded into an image processing system to optimizethe algorithms and the parameter settings Ph.D. Course in Digital Halftoning

6. Objective Image Quality • Two classes of objective quality assessment 1.Mathematically defined measures • Methods based on the Mean Square Error (MSE) 2. Models of the human visual system (HVS) • Methods using the contrast sensitivity function (CSF) Ph.D. Course in Digital Halftoning

7. Image Quality Error Metrics • Function • Derive measure from the point-wise difference between original and the binary halftone • In general, using a fixed threshold at midpoint • Definition Ph.D. Course in Digital Halftoning

8. Image Quality Error Metrics • Correlation between MSE, SNR or PSNR and visual quality is known to be poor • Treats all errors with an equal weight White Noise SNR = 10 dB PSNR = 15,7 dB High-pass Noise SNR = 10 dB PSNR = 15.7 Ph.D. Course in Digital Halftoning

9. Human Visual System • Complicated Non-linear and spatially varying • Assuming linearity and spatial invariance • The human perception system do not have equal response to all spatial frequencies • As the spatial frequencies become higher and higher, our ability to perceive the pattern will be lower and lower • It turns out that our ability to perceive very low frequency patterns also decreases as the frequency decreases • These characteristics can be captured using a contrast sensitivity function (CSF) Ph.D. Course in Digital Halftoning

10. Human Visual Response • Object image by the eye Visual angle: • Sensitivity depends on angular frequency subtended at eye • Compute angular frequency from image size (pixels), printed image size (mm), viewing distance (mm) At Nyquist frequency: Ph.D. Course in Digital Halftoning

11. Contrast Sensitivity Function • Band-pass model [Mannos & Sakrison 1974] • Modified to low-pass [Mitsa & Varkur 1993] Ph.D. Course in Digital Halftoning

12. Contrast Sensitivity Function • Angular dependence in CSF [Sullivan, Miller & Pios 1993] • Mild-drop in visual sensitivity in diagonal directions • The decreased sensitivity along the diagonals and the flattening at low angular frequencies are visible Ph.D. Course in Digital Halftoning

13. Weighted SNR Metric • Weighted SNR by CSF • WSNR measures appropriate when noise is additive and signal independent • Where X(u,v), Y(u,v) and C(u,v) represent the DFT of the input image, output image and CSF, respectively Ph.D. Course in Digital Halftoning

14. WSNR • To find WSNR • Generate unsharpened halftone using modified error diffusion [Eschbach & Knox 1991] • Compute WSNR of unsharpened halftone relative to original image Ph.D. Course in Digital Halftoning

15. A Universal Image Quality Index • Main Features • New Philosophy: switch from error measurement to structural distortion measurement • Mathematically defined and no HVS model is explicitly employed • Universal: Applicable on various image-processing applications and provide a meaningful comparison across different types of image distortions • Easy to apply on images • Low computational complexity Ph.D. Course in Digital Halftoning

16. A Universal Image Quality Index • Application to Images • Compare difference between the original and the binary image • Measure statistical features locally and then combine them together • Sliding window (size 8  8) approach in local region, leading to a quality map • The index value is the average of the quality map Ph.D. Course in Digital Halftoning

17. A Universal Image Quality Index • Definition 1 2 3 Q :Dynamic range[-1, 1] • Combination of three factors • 1. Loss of Correlation (Linear correlation between x and y) • 2. Luminance Distortion (Mean luminance between x and y) • 3. Contrast Distortion (Variance contrast (signal) between x and y) Ph.D. Course in Digital Halftoning