chapter1 color image processing

7/30/2019 Chapter1 Color Image Processing

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Chapter 1

Introduction: Colour Image Processing

1.1 Introduction

Colour is a perceived phenomenon, not a physical property of light [San98]. Among all the

human senses, sight and the perception of colours should be perhaps the most fascinating. Most

of image processing has been associated with binary or black & white (B&W) images. This is

largely because of the high cost and limited availability of sensors and processing resources in

the past. Colour image usually requires about 64K times more data (considering that 8 bits are

needed for B&W and 24 for colour) to process. Colour has really been studied for the last 25

years and only recently low cost sensors and computing power are readily available. With the

appearance of computers with larger amounts of memory and faster speeds, the processing of

colour images is now more plausible to realise.

With cheaper sensors a need to explore different low cost processing is needed. Hence, special

purpose hardware, which gives real-time performance, is a very attractive alternative. This

thesis explores an alternative of the realisation and implementation of colour image processing

in dedicated hardware as opposed to that of using a general-purpose machine, i.e. personal

computer. These realisations would translate into much faster speeds permitting real-time

processing, which are important to various sectors such as industry, medicine, etc.. Also, by

having a dedicated hardware, costs are dramatically reduced.

In this work, a Universal Colour Transformation Hardware (UCTH) system that operates at

real-time video rate is proposed. The UCTH is capable of performing two objectives. First,

represent colour in a convenient way and second, implement limited colour image processing


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algorithms. Since most colour image processing applications rely on an appropriate colour

space to carry out the algorithms and several references exist in the literature, the UCTH

permits transformations for Red Green Blue (RGB) to alternative colour spaces. These can beset-up by the user for a specific application and then realise some particular digital image-

processing task, e.g. colour segmentation, hue shifting, clustering, edge detection, etc.

In order to be able to handle and manipulate colour acquired from capturing devices such as

single-chip or three-chip cameras, it is essential to understand the mechanisms of colour vision,

the representation of colour and the capabilities and restrictions of colour imaging devices. The

next sections of this chapter explore these issues and at the end, the organisation of this thesis is

presented.

1.2 The history of colour

Although colour has always existed, it was Sir Isaac Newtons experiments in Trinity College,

Cambridge in 1666 that established a physical basis for colour [Fau88] [Coh95]. With the use

of sunlight and a prism, Newton discovered that white light could be made to separate into aseries of different colours. Also, he determined that the effective refractive power of the glass

varied according to the position of the colour in this sequence, with red deviating least in angle

(i.e. longest wavelength) and blue the most (i.e. shortest wavelength). The term spectrum was

created by Newton to describe the ghostly effect quality of this effect.

A better understanding of light and colour occurred when Thomas Young in 1801 hypothesised

that human eyes have three receptors and the difference in their responses contributes to the

sensation of colour [Mac93]. He demonstrated that by overlapping three primaries lights having

the principal colours of Red, Green and Blue-Violet he could obtain the secondary colours

Yellow, Cyan and Magenta [Jac94].

Another way of producing additive colour is by using a spinning disc with coloured regions

having adjustable sectors and a central coloured area. During the 1850s James Clerk Maxwell

[Max95], with his discs, found that by adjusting the sectors and spinning the disc fast enough,

the sectors appear to fuse matching the colour in the central area, a phenomenon now referred


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to as trichromatic generalization or trichromacy [Sha97]. Around that time, Helmholtz

[Mac70] explained the distinction between additive and subtractive colour mixing and

explained trichromacy in terms of spectral sensitivity curves of the three colour sensing fibresin the eye. Trichromacy indicated the fact that the human eye has three colour receptors known

as the S, M and L cones for short, medium and long wave sensitivities, respectively, that can

now be determined through psychophysical experiments [Sto93].

Before these measurements were possible, Colour Matching Functions (CMFs) were

determined through psychophysical experiments [Wys00]. Here a normal observer matched any

spectral light to mixture of three fixed-colour primary lights. The observer sees a split

(bipartite) fields where a test colour has to be matched by varying the contribution of the 3

primary lights that generate the mixture. This forms the basis of all colorimetry.

1.3 Colorimetry

The science of colour and its measurement is known as colorimetry. The Commission

Internationale de lEclairage (CIE) is the main organisation responsible of colour metrics andterminology. The first colour specification was developed by the CIE in 1931 and continues to

form the basis of modern colorimetry [CIE86]. The following terms have been defined by the

CIE and are given in [Hun91]:

Brightness: The human sensation by which an area exhibits more or less light.

Hue: The human sensation according to which an area appears to be similar to one, or

two proportions of two of the perceived colours red, yellow, green and blue.

Lightness: The sensation of an areas brightness relative to a reference white in a scene.

Chroma: The colourfulness of an area relative to the brightness of a reference white.

Saturation: The colourfulness of an area relative to its brightness.

A colour, therefore, is a visual sensation produced by a specific spectral power distribution

(SPD) incident on the retina.


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The CIE system works by weighting SPD of an object in terms of the human vision system

(HSV) by providing two different but equivalent sets of CMFs. The first set, know as the

CIERGB (Red-Green-Blue) associated with monochromatic primaries at wavelengths of 700,546.1 and 435.1 nm, respectively [Clu72] and is depicted on the left of Figure 1.1. Their radiant

intensities are adjusted so that the tristimulus values have a constant spectral radiance. The

second set of CMFs, known as the CIEXYZ are shown in right portion of Figure 1.1. A set of

three artificial primariesX, YandZwhere created [Fai98] for CIEXYZ to avoid negative values

appearing in the CIERGB which simplifies operations. CIEXYZ is defined in terms of a linear

transformation of CIERGB and since an infinite number of transformations can be defined in

order to meet this non-negativity requirement this is not a physically realisable space [Sha97].

Figure 1.1: Spectral tristimulus values of the CIE 1931 standard colorimetric observer for

CIERGB (left) and CIEXYZ (right).

1.4 Colour models

Other common terms for colour models include colourspaces, colourcoordinatesystems and

are three-dimensional (3-D) arrangements of colour sensations where colour are specified by

points in these spaces [San98]. The colour models are used to classify colours and to qualify

them according to such attributes as hue, saturation, chroma, lightness or brightness. They are

also further used for colour matching and are valuable resources for anyone working in any

medium, e.g. printing, video, image processing, etc.


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When considering the variety of available colour systems, it is necessary to classify them into a

few categories according to their definitions. Figure 1.2 shows the most typical colour systems

that have been grouped into families of systems. These include hardware-oriented systems,user oriented systems, artificial primary systems, perceptually uniform systems and polar

coordinate systems. The shaded boxes in Figure 1.2 represent the colour spaces considered

during this research. Other families of systems include normalised systems and independent

non-correlated systems. It should be noted that all the colour systems depicted in Figure 1.2

originate from RGB, which is by far the most used colour space for the acquisition of and

display of images by using colour cameras and CRT monitors respectively.

Figure 1.2: Colour Systems and colour spaces. The highlighted colour spaces are

considered for the work presented in this thesis.

CIEXYZ

KodakYCC

IHS

HLS

HSV

TEKHVC

RGB

CMY

CMYK

Primary based

YIQ

YUV

YCrCb

NTSC

PAL (EBU)

Digital

xyz

rgb

TV based

Printing based

Hardware OrientedSystems

Normalised

Systems

Artificial Primary

Perceptual User

Oriented Systems

CIExyY

CIEL*a*b*

CIEu'v'Y

CIEL*u*v*

Perceptually Uniform

Systems

Y,S,H L,C*,Ho

Polar Coordinate

Systems

OHTA KLT

Independent Non-Correlated

Systems


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1.4.1 Hardware oriented systems

These colour systems are device dependant models [Bas92] which means that the colour

depends on the equipment and the set-up used to produce it. As an example by using a

computer CRT monitor to display colours, the colour produced using pixel values of RGB =

0,255,255 (i.e. cyan) will alter as the brightness and contrast is changed on the monitor. In the

same way, if the monitor was to be replaced, the red, green and blue phosphorus used for the

screen will have slightly different characteristics and the colour produced will change.

1.4.1.1 The RGB colour space

In this model, colours are formed by the combination of the three primary colours red, green

and blue, which makes RGB and additive system. It forms the most basic and well-known

colour model and can be seen in Figure 1.3. The different levels for each of the primary colours

can produce a wide range of colours, i.e. gamut[Gon93]. The benefit of this colour system is

the ease of implementation and therefore is widely used for colour cameras and computer

cathode ray terminals (CRT) displays. Among the disadvantages of this colour space we find its

device dependency, the high correlation of colours and the difficulty of perception by a user.

Common practice is to assume that colour similarity is inversely proportional to a distance

metric in that space. This assumption proves inappropriate for RGB, since equal distances in

this colour space rarely match perceived equivalence in similarity [Sea99].

Figure 1.3: Two views of the RGB colour space

Colour image processing algorithms based on RGB can be found in pixel clustering based on

empty or full bins of colour histograms and are reported in [Nov92]. Potential applications of


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RGB to machine vision are discussed from a perspective of physics-based vision and the

calibration procedure in [Mar96]. Zugaj and Lattuati [Zug98] employed a gradient operator,

applied on the multi-image RGB in order to produce edges in colour segmentation. A series ofsurfaces closely modelling how the human eye cone receptors respond to RGB changes give the

red/green and yellow/blue chromatic direction of the surfaces, while rod receptors being

sensitive to lightness variations give the separation of the surfaces. This produces the RGYB

colour geometry and is reported in [War90]. One-chip CCD cameras use interpolation methods

to produce full-colour images in RGB. The design of practical, high quality filter array

interpolation algorithms on a simple image model are discussed in [Ada97] and [Ada98].

The normalised components, which are called chromaticity coordinates, only take into account

chrominance. The chrominance coordinates of the RGB system are denoted rgb, where

( )BGRRr ++= , ( )BGRGg ++= and grb =1 . Colour can alternatively be

represented in the chromaticity diagram by the coordinates ( gr, ) [Wys00] [Van00].

Values of rgb coordinates are much more stable with changes in illumination than RGB

coordinates [Ber87]. For this, rgb has been used in applications such as the stability of our

perception of surface colours despite changes in illumination, i.e. colour constancy [Fin95].Early applications ofrgb can be found in analysis of aerial pictures [Ali79] and edge detection

[Nev77]. Based on the intersection of histograms, Swain and Ballard [Swa91] using an

opponent colour axis derived from rgb created an colour indexing system for image retrieval

based purely on colour properties and not on geometrical properties.

1.4.1.2 The CMY colour space

CMY or Cyan-Magenta-Yellow colour space is a subtractive model and is used primarily in

printing and photography. Printers often include a fourth component, black, to have CMYK.

Black, symbolised by K, is substituted for equal parts of CMY to lower the costs of ink and to

generate a pure black. Subtractive colours are seen when pigments in a object absorb certain

wavelengths of white, while reflecting the rest. The wavelengths that are reflected as opposed

to being absorbed are the ones perceived as colour.


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An application has been found by Verikas et al. [Ver97] to determine colours of inks used to

produce multi-coloured pictures created by printing dots of cyan, magenta, yellow and black

primary colours upon each other through screens having different raster angles. Colourreproduction process many time relies in three (CMY) or four (CMYK) colours and Herzog

[Her96] proposed a new analytical method to represent a surface of a colour gamut using a

closed expression directly in CIEL*a

*b

*based on the similarity of the gamut for the CMY cube.

1.4.1.3 The YIQ colour space

The National Television Standards Committee (NTSC) of the United States uses a colour

specification consisting of luminance (Y) and two difference colour signals called inphase and

quadrature (I and Q) [Hut90]. The YIQ exploits a useful property of our vision system. The

system is more sensitive to changes in luminance than changes in hue and saturation (i.e.

colour); that is, our ability to discriminate spatially colour information is weaker than our

ability to discriminate spatially monochromatic information [Fol90]. This result is an I-axis

encoding chrominance information along a blue-green to orange vector, and a Q-axis encoding

chrominance information along a yellow-green to magenta vector.

Applications of the YIQ space have proven to be useful in colour image coding [Ove95]

[Van94] to obtain image compression. An image retrieval system named PicSOM [Laa99] uses

the Y component to perform image querying on the World Wide Web (WWW). Speech

recognition based on video represented in YIQ for lip tracking combined with acoustic signals

can be found in [Hen95]. Boo and Bose [Boo] proposed a procedure to restore a single colour

image, which has been degraded by shift-invariant blur in the presence of additive stationary

noise. Reduction of up to 50% in the size of the frame buffer used with colour palettes to

display images on CRT computer screens is achieved by using vector quantization in the YIQ

colour space [Wu96].

1.4.1.4 The YUV colour space

This is a specification for the European Broadcast Union (EBU) and PAL and SECAM

television systems in Europe and in many other countries use the system [Car69]. It consists of


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a luminance Ysignal and two colour difference signals Uand Vthat are used as transmission

coordinates. The YUV coordinate system was initially proposed as the NTSC transmission

standard, but was replaced by the YIQ system because it was found that the I and Q signalscould be reduced in bandwidth to a greater degree than U and V for an equal level of visual

quality [Pra91].

The effect of colour quantization schemes on the performance of image retrieval with colour

clustering using YUV, RGB, HSB and CIEL*u*v* can be seen in [Wan98]. A novel technique

for efficient coding of texture to be mapped on 3-D surfaces using digital maps represented in

YUV format is given in [Hor97]. A real-time algorithm for extracting shape parameters from

facial features such as eyes and mouth has been carried out in [Rao96]. YUV colour space has

proved to be useful in the Motion Picture Experts Group (MPEG) encoding [Tor97] [Kru95].

Robotic vision using YUV colour model also is reported in the literature [Sch97] [Nak98].

1.4.1.5 The YCrCb colour space

This is a space that is independent of the TV signal coding systems and is primarily oriented to

digital television [Rob97]. The component Y for luminance is identical to that for YUV and

YIQ. The chromatic information is found in the Cr(colour red) and Cb (colour blue) signals.

Current applications in image compression (e.g. JPEG format) often employ YCrCb model as a

quantization space [Coo93] [Ler95] or also used to encode the overall prefix codes which result

from many forms of image compression algorithms such that they are largely independent of

each other [Whi98]. The chrominance sub-sampling information and the possible degrading of

colour in images is enhanced by a methodology proposed by Schmitz [Sch97a]. Localisation of

facial regions in videophone images using both the luminance and chrominace is another

application of YCrCb researched in [Cha96].


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1.4.2 User oriented systems

Other names given to the user oriented systems include: perceptualcoloursystems,perceptual

orientedsystem or computergraphics colourspace. These systems decouple the lightness,

brightness or value information from the chromatic information.

The best known and most influential of all models based on perceptual principles was proposed

in 1905 by Albert Munsell and is known as the Munsellcoloursystem which is still widely in

use to date [Mun76]. Munsell modelled his system as an orb around whose equator runs a band

of colours as can been observed in Figure 1.4. The axis of the orb is a scale of neutral grey

values with 10 divisions with white at the top and black at the bottom. Extending out, from the

axis at each grey value is a graduation of colour progressing from neutral grey to full saturation.

Munsell introduced three aspects that would describe any thousand of colours; these are

described in Munsell terms as:

Hue: The quality by which we distinguish one colour from another. Munsell selected

five primary colours: red, yellow, green, blue and purple; five intermediate colours:

yellow-red, green-yellow, blue-green, purple-blue and red-purple and made a wheel

defining 100 compass points.

Value: The quality by which we distinguish a light colour from a dark one. Value is a

neutral axis that refers to the grey contents of the colour. It ranges for black to white.

Chroma: The quality that distinguishes the difference from a pure hue to a grey scale.

The chroma axis extends from the value axis at a right angle.

A method of expanding colour images in terms of Munsells three aspects or attributes of

colour perception can be found in [Tom87].

1.4.2.1 The IHS colour space

Known sometimes as the Hue-Saturation-Intensity (HSI, IHS) model and is an intuitive model

for specifying colours. Hue refers to the name given to as colour (e.g. red, yellow, etc.) and is

represented as degrees on a colour wheel with values from o0 (red) to o120 (green) to o240


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(blue) to o360 (red again). Saturation is the purity of the colour. Low saturation (< 20%) results

in grey, regardless of the hue; middle saturation (40% to 60%) produces pastels; and high

saturation (> 80%) results on vivid colours. Intensity is the brightness of a colour and ranges

from 0% (black) to 100% (white). Sometime intensity is also referred to as luminance or

lightness [Gon93].

Figure 1.4: Munsell's colour space.

Several image processing functions can be realised using the IHS colour space, these include

segmenting colour using the three colour attributes: hue, saturation and intensity for a robust

specialised architecture for real-time tracking [Gar96]. Remote Sensing is another popular area

where IHS colour model is appropriate for monitoring various natural resources and

environmental hazards. Geohazard assessment using synthetic aperture radar1 (SAR) and

thematic mapped (RM) images are used to characterise areas affected by landslides and costal

hazards in the lower Fraser Valley within the Canadian Rockies [Sin95]. Other applications

include the study of soil salinity and alkalinity dynamics [Dwi98], image fusioning techniques

to exploit the complementary nature of multi-sensor image data [Sch98] [Sun98],

discriminating areas of hydro thermally altered material in vegetated terrain in central Brazil

1 Many people associate the word aperture with photography, where the term represents the diameter of the lens'

opening. The camera's aperture then determines the area through which light is collected. Similarly, a radar antenna's

length partially specifies the area through which it collects radar signals. The antenna's length is therefore also called

its aperture. In general the larger the antenna, the more unique information you can obtain about a particular viewed

object. With more information, you can create a better image of that object (improved resolution). It's prohibitively

expensive to place very large radar antennas in space, however, so researchers found another way to obtain fine

resolution: they use the spacecraft's motion and advanced signal processing techniques to simulate a larger antenna.

A SARantenna transmits radar pulses very rapidly. In fact, the SAR is generally able to transmit several hundred

pulses while its parent spacecraft passes over a particular object. Many backscattered radar responses are therefore

obtained for that object. After intensive signal processing, all of those responses can be manipulated such that the

resulting image looks like the data were obtained from a big, stationary antenna. The synthetic aperture in this case,therefore, is the distance travelled by the spacecraft while the radar antenna collected information about the object.


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[Alm97] and Mediterranean vegetated coastal area classification [Gri97]. Other areas of study

include the analysis of skin lesions [Fis96].

1.4.2.2 The HSV colour space

The HSV model, created by Smith [Smi78] (also called the HSB model, withB for brightness)

is an user-oriented colour model, based on the intuitive appeal of the artists tint, shade and tone

[Fol90]. The coordinate system is cylindrical and the subset of the space within the model is

defined as a hexcone, as it resembles a six-sided pyramid. Value, orV, ranges from black (0) to

white (1). Hue, orH, is measured by the angle around the vertical axis, with red ato

0 , green at

o120 , blue at o240 , and back to red at o360 . Complementary colours are displaced by o180

and these are opposite one another. The saturation, orS, is the purity of the colour. Therefore,

saturation of 100% in the model represents a pure colour and a saturation of 0% represent a

grey level. Figure 1.5 shows a 3 dimensional model of the HSV colour space.

Figure 1.5: HSV colour space. Image on left represent a full view of the colour space,

whilst view on right is a cross-section.

Some important advantages of the HSV colour space are: good compatibility with human

intuition and decoupling of the chromatic values for the achromatic values. Segmentation and

tracking of faces or facial regions in colour images where skin like regions are determined

based on colour attributes, hue and saturation, is a common application of HSV [Sob96]

[Her99] [Yan00]. Another common area for this colour space is in the research of multimedia

information, e.g. images and video [Meh97] [Xio95] where the images are usually retrieved via

some query method [And99]. Applications can also be found for the identification of biological

objects at microscopic level for cell structures [Pav96] or for automatically detecting danger


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labels on the back of containers [Jr96]. Based on the extraction of gradient discontinuities,

edge detection can be achieved using HSV [Tsa96]. Robotic vision [Pri00] and autonomous

vision-based avoidance systems [Lor97] have also been reported using this model.

1.4.2.3 The HLS colour space

Another model based on intuitive colour parameters is the HLS system used by Tektronix

[Tek90] for some of its terminals. This model has a double-cone representation that can be

thought as a deformation of HSV, in which white is pulled upward to form the upper hexcone

from the 1=V plane. The three parameters in this model are called hue (H), lightness (L) and

saturation (S) and can be seen in Figure 1.6. Hue has the same meaning as in the HSV and IHS

models. The vertical axis is this model is called lightness. At 0=L we have black, and white is

at 100=L %. Grey scale is along the L axis and the pure hues lie on the 50=L % plane.

Saturation gives the relative purity of colour, hence when =L 50% and =S 100% a pure colour

is generated.

Tektronix later created the TEKHVC (Hue, Value and Chroma) [Bas92] colour system, that

resembles the HLS only in shape but was created to have a perceptually uniform colour space

in which measured and perceived distances between colours are approximately equal [Fol90].

Some applications using HLS can be found using cluster detection combined with probabilistic

relaxation, where clusters are extracted for specific colour from HLS to locate tumours from a

bladder image [Che98]. In order to visualise trajectories in higher dimensional dynamical

systems, the HLS model has been employed in [Weg97].

1.4.3 Perceptually uniform systems

The colour models, developed by the Commission Internationale de lEclairage (CIE) [CIE86]

have the following objectives:

1. To be completely independent of any device or other means of emission or

reproduction.


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2. Perceptual colour differences recognised as equal by the human eye would correspond

to equal Euclidean distances.

Figure 1.6: The HLS colour space

As perceptually uniform colour spaces of this kind, two spaces recommended by the CIE in

1976 are mainly in use today. These are the CIEL*a

*b

*space (used for reflected light) and the

CIEL*u

*v

*space (mainly used for emitted light) [Wys00].

The CIE colour model was developed to match as closely as possible on how humans perceive

colour. The key elements of the CIE model are the definitions of the standard sources and the

specifications for a standard observer [Fai98]. The following CIE standard sources were

defined in 1931:

Source A: A tungsten-filament lamp with a colour temperature of 2845K.

Source B: A model of noon sunlight with a temperature of 4800K.

Source C: A model of average daylight with a temperature of 6500K.

Source D: Illuminant called daylight D series. Illuminant D65 (D65) with a temperature

of 6500K is the most common referenced.


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1.4.3.1 The artificial primary CIEXYZ colour space.

As mentioned in Section 1.3, the CIE standard considered the tristimulus values for red, green

and blue to be undesirable for creating a standardised colour model because of their inclusion

of negative values (Figure 1.1, left side) which were difficult to mathematically manipulate in

1931. Instead, they use a mathematical formula to convert the RGB data to a system that uses

only positive integers in order to simplify operations. The reformulated tristimulus values were

given identifiers XYZ and are shown in Figure 1.7. These values do not directly correspond to

red, green and blue but are a close approximation. The curve for the Yvalue is equal to the

curve that indicates the human eyes response to the total power of the light source. For this, Y

is called the luminance factor and the XYZ values have been normalised so that Yalways has a

value of 100 [Wys00].

Figure 1.7: The CIEXYZ artificial primary.

Obtaining the XYZ tristimulus values is only part of defining the colour and the colour itself is

easier understood in the term of hue and chroma. To make this possible, CIE used XYZ

tristimulus values to formulate a set of normalised chromaticity coordinates that are denotedxyz

(lowercase XYZ).

The chromaticity coordinates are used in conjunction with a chromaticity diagram with the

most familiar one being CIEs 1931 xyY, or CIExyY [CIE86], chromaticity diagram. Thex and

y serve as a locator for any value of hue and chroma. The third dimension, Y, is the lightness or

luminance of the colour. It extends to white from a spot perpendicular in thex andy plane. As


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the Yvalue increases and the colour becomes lighter, the range of colours, orgamut, decreases

so that the colour space at 100=Y is just a small section of the original area.

Many attempts have been made to have chromaticity diagrams which are perceptually more

uniform. Although there has not been a successful attempt to convert a nominal scale into an

interval sacke, it is worth mentioning one of the results obtained. This is actually the

chromaticity diagram currently recommended by the CIE for general use: the CIE 1976

Uniform Chromaticity Scales (UCS) [CIE86] [Fai98]. CIEuv

Y represents this system, where u

and vare the coordinates of the chromaticity diagram and Ythe luminance.

Applications using CIExyY alone are not commonly found in literature, but an automatic

system for the detection of human faces combining skin-colour image segmentation with shape

analysis showing cumulative distributions and comparing them to HSV is given in [Ter98].

1.4.3.2 The CIEL*a

*b

*colour space

The CIEL*a

*b

*system was adopted by CIE in 1976 as a model that better showed uniform

colour spacing in their values [CIE86]. It is an opponent colour system based on the earlier

(1942) system of Richard Hunter [Hun91] [Rob90] calledL, a, b. It consists of a (a*, b

*) colour

plane that maps the hue of a colour on two dimensions: a (horizontal) dimension, separating red

colours on the right (a+) from green colour on the left (a-), and the b (vertical) dimension,

separating yellow colours at the top (b+) from blue at the bottom (b-). The position of a colour

in the space represents the overall contribution of red or green, and blue or yellow to its hue.

Orange is a combination of red (a+) and yellow (b+), so orange appears in the first quadrant

(a+, b+) of the space. This is clearly depicted in Figure 1.8.

Many colour image processing algorithms, such as coding, segmentation, gamut mapping, etc.

choose CIEL*a

*b

*colour space for its desirable properties [Woe96] [Dub97]. Using hue-

correcting look-up tables, Braun and Fairchild [Bra98] made a series of experiments to test the

utility of using constant hue visual data to linearize the CIEL*a*b* space with respect to hue and

present their results.


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Figure 1.8: The CIEL*a

*b

*colour space. Figure on left shows the main axis and figure on

right a 3-D model of the colour space.

Perhaps, CIEL*a

*b

*is one of the more popular colour spaces for image processing and it has

been used in a number of applications and a few examples are listed next.

Colour removal of dyes that cause pollution originating from textile mill has been studied in

[Yeh95]. Vallart et al. [Val94] used CIEL*a*b* for the measurement of colour for the study of

painted works of art. A model and mathematical formulation for describing the light scattering

and ink spreading phenomena in printing can be found in [Emm00]. Using the L* and b*

(yellowness) parameters, a method was proposed in [Bha97] to control the temperature of

barrel and screw speed for the extrusion of a rice blend. Techniques for examining the effects

of heating soymilk were described in [Kwo99]. A method for embedding invisible information

in colour images (i.e. watermarking) was proposed in [Fle97].

Extensive experimentation has been done on CIEL*a*b* areas such as gamut mapping

techniques [Mon97], obtaining figures of merit to evaluate how close CIEL*a

*b

*matches the

actual perceived accuracy for cameras and scanners [Sha97a] [Har98] [Har99] [Har00].

In order to measure colour reproduction errors of digital images, Zhang and Waddell [Zha96]

proposed and extension to the CIEL*a

*b

*colour metric called s-CIEL

*a

*b

*and applications of

this extension can found in [Zha97].


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1.4.3.3 The CIEL*u

*v

*colour space

CIEL*

u*

v*

originates from a series of chromaticity diagrams that where inadequate because the

two-dimensional diagram failed to give uniformly-space visual representation of what is

actually a three-dimensional colour space [Wys00]. It originated first from CIExyY in 1931,

next a new set of values ),( vu that presented a visually more accurate two-dimensional model

was proposed by CIE, giving CIEuvY. However, this was still found unsatisfactory and in 1975,

CIE proposed modifying the ),( vu diagram yielding the new )','( vu values creating CIEuv

Y

that has much better visual uniformity. The final successor, in an attempt to further improve the

)','( vu diagram, gave the CIEL*u

*v

*, whereL replaces Y. This colour space is also based on the

opponent-colour theory, which models the human colour vision. In this colour space u*

represents the red-green coordinate, whilst v*

axis represents yellow-blue. TheL*

axis describes

variations in lightness. Shown in Figure 1.9 is a 3-D image of the CIEL*u*v* colour space.

Figure 1.9: The CIEL*u

*v

*colour space.

Many application can be found based on the CIEL*u

*v

*colour space. It has proved very useful

in colour image segmentation methods that use a clustering approach [Sch93] [Uch94] [Hea96].

Recognition and localization of two-dimensional objects on a known background is another

application [Kaa97]. Using lightness and saturation, Liu and Yan [Liu94a] enhanced edges on

colour images. Histogram manipulation is also popular in CIEL*u*v* to enhance images [Mls96]

and to retrieve images from video databases [Par99].

A study to examine the relationship between colorfulness judgments of images of natural

scenes and statistical parameters of chroma distribution over the images can be found in

[Yen97]. An identification experiment of naming 35 colours with no previous training was


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done by [Der95] and an association of CIE colorimetry and colour displays is given by Schanda

[Sch96].

1.4.4 Polar coordinate systems

Polar coordinate systems are basically a rectangular to polar coordinate transformation from a

particular colour space. For TV colour spaces (e.g. YIQ, YUV and YCrCb) a vector having a

magnitude S(Saturation) and an angleH(Hue) is obtained from the chroma channel expressed

in rectangular form, whilst the luminance Yremains unaltered. For perceptually uniform colour

spaces (e.g. CIEL*u*v* and CIEL*a*b*) a vector is obtained with its magnitude given by C*

(Chroma) and an angle oH (Hue). Refer to Figure 1.2.

1.4.5 Independent non-correlated systems

These systems are also known as the statistically independent component systems. They can be

determined by different methods in order to obtain non-correlated components. As shown in

Figure 1.2, Ohta [Oht80] defines a colour system using I1,I2 andI3 using the Karhunen-Loeve

Transformation (KLT) [Loe55]. This transform also is commonly referred to as the

eigenvector,principalcomponent, orHotellingtransform and is based on statistical properties

of vector representations. These systems are not included or studied in this thesis, but are

suggested in Chapter 7 as future research.

1.5 Motivation and objectives behind this work

After reviewing the literature for colour image processing algorithms and the most frequently

colour spaces used for their implementations it was decided to think of a common methodology

that could realize transformations from RGB to most of them. RGB is by far the most widely

used colour space for image acquisition (e.g. still cameras, video cameras, etc.) and displaying

images (e.g. CRT monitors). With colour image processing becoming increasingly more

popular and with the drop in cost of imaging devices and hardware it is now possible to face


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many challenges in optimizing design into a relatively low cost technology. For this, it was

decided not only to devise a universal transformation procedure, but to also implement it in

hardware with the objective of having a low cost and high-performance system. The objectivesof this research are summarized next:

1. Devise a common straightforward methodology capable of converting RGB to the most

commonly used colour spaces reported in literature taking into account that images are

obtained from charged coupled devices (CCD) still-cameras or video-cameras that

generate outputs in the RGB colour space. Also, images will frequently be displayed in

devices based on RGB (e.g. CRT computer monitors).

2. Analyze and manipulate the conversion methodology considering always that it should

be implemented in hardware.

3. When implemented in hardware, real-time conversions rates should be expected,

matching or superseding the frame-rates given by video cameras with full resolution

and colour depth.

4. Design an architecture flexible enough to permit expansion and interconnectability with

other hardware elements in order to perform some colour image processing algorithms

as well.

The colour spaces that will be considered during this work are summarized in Table 1.1

Colour System Sub-system Colour space considered

Television based systems YIQ, YUV, YCrCbHardware oriented

system Printing systems CMY

Perceptual user oriented

systemIHS, HLS, HVS

Artificial primary system CIEXYZPerceptually uniform

system CIE systems CIExyY, CIEL*a*b*,

CIEuv

Y, CIEL

*u

*v

*

Table 1.1: Colour spaces considered during this research

Some hardware applications for real-time colour image processing and colour space

transformations can be found in the literature. A real-time tracking system based on Field

Programmable Gate Arrays (FPGAs) based on colour segmentation and using the IHS colour

space was reported in [Gar96]. FPGAs where also used by Mribout et al. [Mer93] to


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implement edge detection and edge tracking, while Adraka [And96] devised a dynamic

hardware video processing platform. Reconfigurable logic [Woo98] using the XC6200 from

Xilinx

is another methodology of implementing real-time processing. Very Large ScaleIntegration (VLSI) implementations for Application Specific Integrated Circuits (ASICs) and

manipulation of digital colour images in the IHS colour space can be found in [And95] and

using the CIEL*a

*b

*in [And95a].

Integrated circuit manufacturers have been also producing dedicated circuitry for colour image

processing applications. Altera

has a RGB to YCrCb and YCrCb to RGB colour space converter

operating in real-time [Alt97]. Edge detection [Atm00] and 33x convolution [Atm00a] are also

possible using Atmel AT6000 FPGAs. Crystal semiconductors also created a digital colour-

space processor [Cry97] operating in the YCrCb colour space for CCD cameras.

1.6 Organisation of this thesis

Chapter 2 first looks at the equations needed to make transformations from RGB to alternative

colour spaces. Most of the image acquisition devices are based on the RGB colour space;

therefore this chapter reviews the algorithms, equations, procedures reported in literature to

obtain conversions. Next and after analyzing these algorithms a generalization procedure will

be proposed which is suitable to cope with all variants of the transformation procedures. This

will be done having in mind that it should also be suitable for hardware implementation. Three

stages will be identified that will cope with every possible transformation scenario and are

explained. Moreover, the last section of the chapter determines all the ranges of possible values

used by the generalised conversion procedure. This is of vital importance for hardware

implementation to consider the resolution and size of all units that will carry out the arithmetic

operations and to determine the word and bus sizes.

Chapter 3 will explain in detail how the generalized transformation methodology described in

Chapter 2 is realized in hardware. A Universal Colour Transformation Hardware (UCTH)

based on Field Programmable Gate Arrays (FPGAs) and Look-up Tables (LUTs) capable of

performing real-time colour transformations is outlined and implemented here. LUTs will

contain the final values of the resulting transformed colour space, whilst the FPGAs will be


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reducing the size and generating the address buses for each and every look-up table. To further

simplify equations, a cascaded LUT configuration is also proposed and implemented. This

chapter will also be covering how floating-point numbers will be represented using fixed-pointvalues on which mathematical operations can be performed minimizing quantization errors. The

next sections of this chapter will be devoted to implement each of the three stages mentioned in

Chapter 2, which are: a Matrix Multiplier (MM), a Functional Mapping Uunit (FMU) and

Switching Interconnect Logic (SIL). At the end of Chapter 3, the devices used for the

implementation will be described.

Chapter 4 will be devoted to fully test the operation and functionality of the UCTH. Two tests

will be performed: namely a static test with fixed images and a dynamic test with video images.

During the static tests, the results generated by modeling the transformations in software,

simulating the circuitry using the design entry tools and hardware simulations will be verified

and conclusions will be drawn. A formal verification procedure is established to demonstrate

the functionality and precision of the architecture as a whole. With the dynamic testing of the

circuitry, conversion speeds will be obtained to prove the UCTH capability to handle real-time

transformation speeds. A series of support circuitry and units are designed to interface the

UCTH with input and output devices. These will include, a Colour Conditioning Unit (CCU), a

Digitizing Unit (DU), a Filtering Unit (FU) for noise removal and finally generating video

signals that can be displayed on a CRT monitor. Furthermore, this chapter will be incorporating

a section to the UCTH named the colour image processing unit (CIPU) that will be able to

carryout some image processing algorithms to demonstrate the flexibility of the UCTH.

Chapter 5 will be implementing two different colour image-processing algorithms using the

CIPU. First, an image segmentation algorithm based on colour clustering will be realised. It

will be based on the CIEL*

a*

b*

colour space. Regions of pixels will be grouped together based

on their colour properties and delimited by planes creating a cluster volume. Second, a colour

defective vision graphics display will be put into operation also showing the versatility of the

UCTH and the CIPU module. Basically it permits people with no colour deficiencies,

whatsoever, to see through the eyes of people with some kind of colour blindness. The

objectives and uses of these realisations will also be explained.

Chapter 6 deals with the design and implementation in hardware of a 2-D median filter for the

removal of impulse noise from images. By using a novel rank adjustment technique that


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prevents implicit sorting and moving values around, a highly repetitive structure is obtained

which is suitable for hardware completion. A clear procedure of how the technique works is

given by means of an illustrative example. The filter is capable of operating in real-time givinga median output value every clock cycle regardless the size of the input mask. The front-end of

the filter will be converting a linear stream of pixels originating from a CCD camera to a matrix

form with the aid of First-In First-Out (FIFO) memories. At the back-end of the filter, a field

memory will be holding the filtered channel. This chapter also reviews more appropriate used

filters for higher dimension spaces, i.e. vector directional filters (VDF) and vector median

filters (VMF).

Chapter 7 contains the conclusions and suggestions for future work. Comments and

conclusions will be given for the results obtained in every chapter. Finally, suggestions for the

line of research that can be followed in this work will be highlighted.

chapter1 color image processing

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