A Review in Quality Measures for Halftoned Images

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A Review in Quality Measuresfor Halftoned Images

• Student Per-Erik Axelson

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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

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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

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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

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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

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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)

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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

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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
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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)

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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
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Contrast Sensitivity Function

• Band-pass model Mannos Sakrison 1974
• Modified to low-pass Mitsa Varkur 1993

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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

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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

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WSNR

• To find WSNR
• Generate unsharpened halftone using modified
  error diffusion Eschbach Knox 1991
• Compute WSNR of unsharpened halftone relative to
  original image

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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

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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

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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)