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

[email protected]: -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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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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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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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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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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Fig. 9: Visible color spectrum.

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Fig. 10: Visible wavelengths.

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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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Fig. 11: Hue representation.

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Fig. 12: Hue representation.

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Fig. 13: HSV color model representation.

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Fig. 16: HSV color model.

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Fig. 17: HIS color model.

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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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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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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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Fig. 23: Monitor phosphor.

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

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

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

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

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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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Fig. 33: RGB color model.

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Fig. 34: CIE Lab color models.

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Fig. 35: color copier.

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

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Fig. 37: System overview.

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Fig. 38: Pseudo color for detection.

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Fig. 39: Pseudo color example.

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Fig. 40: Color manipulation.

The EndThe End

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