A Review in Quality Measures for Halftoned Images Student Per-Erik Axelson

Ph.D. Course in Digital Halftoning 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 29/11/2018 Ph.D. Course in Digital Halftoning

Objective Image Quality Image quality paradigm Original Image Reproduced Image Most important demand In General, we want binary image to match continuous-tone original as closely as possible 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

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 optimize the algorithms and the parameter settings 29/11/2018 Ph.D. Course in Digital Halftoning

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) 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

Ph.D. Course in Digital Halftoning 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) 29/11/2018 Ph.D. Course in Digital Halftoning

Ph.D. Course in Digital Halftoning Human Visual Response Sensitivity depends on angular frequency subtended at eye Compute angular frequency from image size (pixels), printed image size (mm), viewing distance (mm) Object image by the eye Visual angle: At Nyquist frequency: 29/11/2018 Ph.D. Course in Digital Halftoning

Contrast Sensitivity Function Band-pass model [Mannos & Sakrison 1974] Modified to low-pass [Mitsa & Varkur 1993] 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

Ph.D. Course in Digital Halftoning 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 29/11/2018 Ph.D. Course in Digital Halftoning

Ph.D. Course in Digital Halftoning WSNR To find WSNR Generate unsharpened halftone using modified error diffusion [Eschbach & Knox 1991] Compute WSNR of unsharpened halftone relative to original image 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

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 29/11/2018 Ph.D. Course in Digital Halftoning

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) 29/11/2018 Ph.D. Course in Digital Halftoning