in the name of allah - sharif university of...

In the name of Allahthe compassionate, the mercifulthe compassionate, the merciful

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S. KasaeiSharif University of Technology

Room: CE307

skasaei@sharif.eduMail: -EHome Page: http://ce.sharif.edu

http://ipl.ce.sharif.eduhttp://sharif.edu/~skasaei

Digital Image ProcessingDigital Image Processing

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Chapter 3

Image Perception

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Introduction

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Introduction

Visible Light Imaging Model.

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Introduction

Irradiance Light

Radiance Light

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Introduction

In presenting the output of an imaging system to a human observer, it is essential to consider how it is transformed into information by the viewer.

Understanding of the visual perceptionprocess is important for developing measures of image fidelity.

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Introduction

Visual image data represents spatial distribution of physical quantities (luminance & spatial frequencies).

Perceived information may be presented by attributes (brightness, color, & edges).

The goal is to study how the perceived information may be represented quantitatively.

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Introduction

Fig. 1: Simplified diagram of a cross section of the human eye.

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Introduction

Fig. 2: Cross section of the eye.

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Introduction

Color is about spectrum & wavelength.Light is defined as the electromagneticradiation that stimulates our vision response.It consists of an electromagnetic wave, with wavelengths in the range of 380-780 nm, in which the human eye is sensitive.The energy of light is measured in flux, with the unit of watt.It is expressed as a spectral energy distribution.

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Introduction

Fig. 3: The electromagnetic spectrum.

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Color Representation

Fig. 4: Visible wavelengths.

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Introduction

Fig. 5: Typical relative luminous efficiency function of human eye.

Blue

Green

Red

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Introduction

The luminance of an object is independentof the luminance of the surrounding objects.

The (apparent) brightness of an object is the perceived luminance & depends on the luminance of the surround.

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Introduction

Fig. 6: Simultaneous contrast. Top: Middle squares have equal luminancebut do not appear equally; Bottom: Middle squares appear almost equally bright, but their luminance are different.

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Introduction

The spatial interaction of luminance from an object & its surround creates a phenomenon called the match band effect.

It shows that brightness is not a monotonic function of luminance.

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Introduction

Fig. 7: Mach band effect. (a) Gray level bar chart; (b) Luminance versus brightness.(a) (b)

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Introduction

Measurement of the mach band effect can be used to estimate the impulse response of the visual system [h(n)].

The negative lobes [in h(n)] indicate that the neural signal at a given location has been inhibited by some of the laterally located receptors.

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MTF of the Visual System

A direct measurement of the visual system’s modulation transfer function (MTF), is possible by considering a sinusoidal grating of varying contrast (ratio of the Max to Min intensity) & spatial frequency.

Observation of this Fig. shows the thresholds of visibility at various frequencies.

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MTF of the Visual System

Fig. 8: Modulation transfer function (MTF) of the human visual system. (a) Contrast versus spatial frequency sinusoidal grating; (b) Typical MTF plot.

cpd

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MTF of the Visual System

Human visual system is most sensitive to mid-frequencies (3~10 cycles/degree) & least sensitive to high frequencies.

Contrast sensitivity also depends on orientation of the grating (max for horizontal & vertical grating).

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MTF of the Visual System

Angular sensitivity variations are within 3dB (Max. deviation at 45 degree).

Spatial frequency components, separated by about one octave, can be detected independently by observers.

Thus, visual system contains a number of visual system contains a number of independent spatial channels, each tuned to a independent spatial channels, each tuned to a different spatial frequency & orientation angle.different spatial frequency & orientation angle.

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Image Fidelity Criteria

There are 2 types of fidelity criteria: subjective & quantitative.

Subjective criteria use rating scales such as goodness scales & impairment scales.

Quantitative criteria includes: average LSE, MSE, average MS, SNR, PSNR, & frequency weighted MS.

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Subjective Criteria

Table 1: Image goodness scales.

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Subjective Criteria

Table 2: Image impairment scales.

Sk: score,

nk: # observers, n: # grades.

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Quantitative Criteria

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Quantitative Criteria

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Quantitative Criteria

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Quantitative Criteria

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Quantitative Criteria

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Color Representation

Use of color is not only more pleasing but it also enables us to receive more visual information.

While human can perceive only a few dozen gray levels, have the ability to distinguish between thousands of colors.

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Color Representation

Fig. 9: Visible color spectrum.

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Color Representation

Fig. 10: Visible wavelengths.

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Color Representation

Human color perception is 3 dimensional.

The perceptual attributes of colors are brightness, hue, & saturation.

Brightness presents the perceived luminance.

Hue refers to its “redness”, “greenness”, ...

Saturation is the aspect of perception that varies most strongly as more while light is added.

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Color Representation

Fig. 11: Hue representation.

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Color Representation

Fig. 12: Hue representation.

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Color Representation

Fig. 13: HSV color model representation.

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Color Representation

Fig. 14: HSV color model representation.

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Color Representation

Fig. 15: HSV color model representation.

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Color Representation

Fig. 16: HSV color model.

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Color Representation

Fig. 17: HIS color model.

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Color Representation

Fig. 18: HIS color model.

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Color Representation

Fig. 19: HIS color model.

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Color Representation

For monochromatic light sources, differences in hues are manifested by the differences is wavelengths.

These definitions are somewhat imprecise.

Hue, brightness,& saturation all changewhen either the wavelength, the intensity, the hue, or amount of white light in a color is changed.

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Color Representation

A human observer perceives color through the stimuli of 3 different pigmented cones.

Fig. 20: Typical absorption spectra of cons in the retina, as a function of wavelength.

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Color Representation

Fig. 21: Colors as relative responses (ratios).

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Frequency Responses of Cones

Some colors cannot be produced using only positively weighted primaries.Some colors need negative amounts of primaries.

Fig. 22: Analysis spectrum of cone responses has negative lobs.

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Color Representation

Fig. 23: Monitor phosphor.

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Color Representation

A weighted sum of primaries produces a color that cannot be distinguished by an observer from the color of the spectrum.

Fig. 24: Additive color model

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Color Representation

Fig. 25: Primary & secondary colors of light & pigments.

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Color Representation

Primary colors for reflecting sources (also known as secondary colors):

Cyan, Magenta, & Yellow (CMY).Example: CMY color printer,

Cyan ink absorbs red light.Magenta ink absorbs green light.Yellow ink absorbs blue light.C+M+Y absorbs all light: Black!

Color printer works by using cyan, magenta, yellow, & black (CMYK) dyes. The dye:

Acts like a narrow-band filter.Saves ink.Produces higher-quality black.Increases gamut.

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Color Representation

Fig. 26: Single chip color CCD.

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Color RepresentationTable 3: Color coordinate systems

[Commission Internationale de L’Eclairage (CIE)].

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Color RepresentationTable 3: Color coordinate systems (Cntd).

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Color RepresentationTable 3: Color coordinate systems (Cntd).

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Color Representation

Table 4: Transformation from NTSC Receiver Primary to other coordinate systems.

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Color Representation

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Color Representation

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Color Representation

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Color Representation

Fig. 27: CIE XYZ (only positive lobs, but not orthogonal).

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Color Representation

Fig. 28: CIE XYZ chromaticity diagram.

Visible colors

Many pointsin XYZ do notcorrespond to visible colors!

Some hues do not correspond

to a pure spectrum

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Color Representation

Fig. 29: CIE XYZ chromaticity diagram.

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Color Representation

Fig. 30: Monitor gamut of CIE XYZ chromaticity diagram.

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Color Representation

Fig. 31: CIE XYZ chromaticity diagram.

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Color Representation

Fig. 32: The RGB safe-color cube.

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The Infamous Gamma Curve

A gamma curve is used for many reasons:CRT response:

The relation between voltage & intensity is non-linear.Color quantization:

We do not want a linear color resolution.More resolution for darker tones.Because we are sensitive to intensity ratios.

Perceptual effect:We perceive colors in darker environment less vivid.Hunt & Stevens effect.

Contrast reduction:Keep some contrast in the highlights.

γxx→

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Color Representation

Fig. 33: RGB color model.

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Color Representation

Fig. 34: CIE Lab color models.

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Color Representation

Fig. 35: color copier.

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Color Representation

Fig. 36: Different gamut.Blue: CRT, Red: 4-color printing.

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Color Representation

Fig. 37: System overview.

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Color Representation

Fig. 38: Pseudo color for detection.

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Color Representation

Fig. 39: Pseudo color example.

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Color Representation

Fig. 40: Color manipulation.

The EndThe End