PPM PART I Media and Media formats Images A) COLOR IMAGES A) - PowerPoint PPT Presentation

PPM PART I Media and Media formats

• Images A) COLOR IMAGES A) Color spaces B) Image Formats B) IMAGE STANDARDS A) JPEG B) JPEG 2000

Color Images: color spaces

Color perception Light source Observer (human eye) Ε ( λ Ε ( λ ρ ( λ ) ) ) Incident light Reflected light • Humans perceive color light through the eye sensor. Color is determined by the relatve radiant power distributon of the incident light, the refecton of the materials and the characteristcs of the observer. • The appearance of an object is determined by its refectance and the visible wavelenghts of the light it is exposed with (and angle).

The human eye sensor • The human eye sensor operates in the wavelenght interval 350nm - 780nm (infrared is beyond 780nm and ultraviolet below 350nm). The visible spectrum is therefore comprised between 384THz and 857THz (THz = 10 12 Hz )

Eye response: Luminance and Chrominance + Achromatic Red cone + + (Luminance) + + Rods - Red-Green + + Green cone (Chrominance) Yellow - + Blue-Yellow Blue cone (Chrominance) • Human eye rods and cones have sensitvity to Luminance and Chrominance, respectvely. Eye has higher sensitvity for Luminance

Human eye spectral integration  Color sensaton of humans is obtained from the combinaton of the responses of the three types of cones, according to the amount of light E the object refectance ρ and the cone type sensibility S t . ρ ρ ρ

Radiant and illuminated objects  Humans perceive colors according to two distnct processes, depending on whether the object observed is a light source object or is illuminated by an external sorce.  In the frst case we perceive the light that is radiated by the object In the second case we perceive the light that is not absorbed (i.e. is refected).  The color perceived is the color of the illuminant less the color absobed by the object.

Reflectance of Human Skin ρ ( λ ) Black • Diferent objects have diferent refectances in the visible spectrum

Colour Image Formation Observer (Camera) f C ( λ ) Ε ( λ ) Ε ( λ ρ ( λ ) ) Incident light Reflected light • Colour image formaton is determined by the relatve radiant power distributon of the incident light, the refecton of the materials and the characteristcs of the observer device. • The appearance of an object is determined by its refectance and the visible wavelenghts of the light it is exposed with (and angle).

( λ ) Observer/Sensor sensitivity f Eye Response Camera Response ∫ = λ ρ λ λ λ R E ( ) ( ) f ( ) d Skin R λ ∫ = λ ρ λ λ λ G E ( ) ( ) f ( ) d Skin G λ ∫ = λ ρ λ λ λ B E ( ) ( ) f ( ) d Skin B λ ∆ A / x ∑ ∫ ≈ ∆ ∆ f ( x ) dx f ( i x ) x = i 1 • Refected light spectrum can be represented by a 3 element vector, with values which are the proportons of each of the primary colors red (R), green (G) and blue (B) used to produce it. These are the tristmulus values. • Considering Tristmulus: RGB values of camera = Colour * Tristmulus.

RGB cameras • Digital images are nowadays obtained from photographic digital cameras that use CMOS or CCD sensors to acquire three color signals in the red (R) green (G) and blue (B) wavelenghts. These cameras ofen operate with a variaton of the RGB space in a Bayer flter arrangement: green is given twice as many detectors as red and blue (rato 1:2:1) in order to achieve higher luminance than chrominance resoluton. • The sensor has a grid of red, green, and blue detectors arranged so that the frst row is GBGBGBGB, the next is RGRGRGRG and that sequence is repeated in subsequent rows. For every channel, missing pixels are obtained by interpolaton to build up the complete image.

Demosaicing algorithms red green blue Bayer filter samples reconstructed after original demosaicing • A pixel only records one color out of three and cannot determine the color of the refected light. An algorithm is needed to estmate for each pixel the color levels for all color components, rather than a single component. • To obtain a full color image demosaicing algorithms are used that interpolate a set of complete green, red, blue values at each point. This is done in-camera producing a JPEG image. For example, demosaicing can be done with bilinear interpolaton the red value of a non-red pixel is computed as the average of the two or four adjacent red pixels, and similarly for blue and green….

Truecolor representation • Truecolor is a method of representng and storing graphical image informaton. Truecolor defnes 256 (2 8 ) shades of red, green, and blue for each pixel of the digital picture, which results in 256 3 or 16,777,216 (approximately 16.7 million) color variatons for each pixel.

Color spaces • A color space is a three-dimensional defniton of a color system. The atributes of the color system are mapped onto the coordinate axes of the color space. • Diferent color spaces exist: each has advantages and disadvantages for color selecton and specifcaton for diferent applicatons: – Some color spaces are perceptually linear, a change in stmulus will produce the same change in percepton wherever it is applied. Other colour spaces, e.g. computer graphics color spaces, are not linear. – Some color spaces are intuitve to use, i.e. it is easy for the user creatng desired colors from space navigaton. Other spaces require to manage parameters with abstract relatonships to the perceived color. – Some color spaces are ted to specifc equipments while others are equally valid on whatever device they are used. – ….. Models Applications CIE XYZ Colorimetric calculations Colorimetiric Device- Non-uniform Storage, processing, oriented spaces analysis, coding, color TV RGB, YIQ, YCC Uniform spaces Color difference evaluation, L* a* b*, L* u* v* analysis, color management systems Device- HSI, HSV, HSL, Human color perception, oriented and I 1 I 2 I 3 .... computer graphics User-oriented Munsell Human visual system

CIE color matching experiment • The frst color matching experiments were devised in late 1920s to characterize the relatonship between the physical spectra and the perceived color, measuring the mixtures of diferent spectral distributons that are required for human observers to match colors • The experiments were conducted by using a circular split screen 2° in size (the cone distributon in the human fovea). On one side of the feld a test color was projected and on the other side, an observer-adjustable color was projected, that was a mixture of three monochromatc ( single- wavelength ) primary colors, each with fxed chromatcity, but with adjustable brightness • Not all test colors could be matched using this technique. When this was the case, a variable amount of one of the primaries was allowed to add to the test color. The amount of the primary added to the test color was considered to be a negatve value

CIE primaries and color matching functions • In 1931 CIE standardized the RGB color matching functons obtained using three monochromatc primaries at wavelengths of 700 nm ( red ), 546.1 nm ( green ) and 435.8 nm ( blue ). The color matching functons are the amounts of primaries needed to match the monochromatc test primary. • Rather than specifying the brightness of each primary, the curves were normalized (scaled) to have constant area under them. The tabulated numerical values of these functons are known as the CIE standard observer (CIE 1931 2° standard observer). They roughly correspond to colour sensatons of red , green and blue .

CIE XYZ color model • Having developed an RGB model of human vision using the CIE RGB matching functons, the commission developed another color space that would relate to the CIE RGB color space by a linear transformaton. The new space would be defned in terms of three new color matching functons and had some nice propertes such as (among them): • The new color matching functons were to be everywhere greater than or equal to zero. • The color matching functon would be exactly equal to the photopic luminous efciency functon for the " CIE standard photopic observer ”. • The three new color-matching functons , called yield the CIE XYZ tristmulus values X , Y , and Z . The Y parameter was a measure of the brightness or luminance of a color, Z was quasi-equal to blue stmulaton, and roughly red . • The CIE XYZ color space serves as a basis from which other color spaces are defned .

CIE xyY color model • By normalizing XYZ i.e. dividing by ( X+ Y+Z ) derived values are obtained referred to as x,y,z . In that x + y + z = 1 the chromatcity of a color can be specifed by two parameters x y, of the three normalized values. • By intersectng the XYZ space with plane X+Y+Z=1 and projectng this intersecton on the x-y plane we obtain the CIE Chromatcity Diagram; xy values are referred to as chromatcity values • The derived space xyY is widely used in practice to represent colors.

RGB color model • The CIE RGB color space is one of many RGB color spaces, distnguished by a partcular set of monochromatc (single-wavelength) primary colors. It has a linear relatonship with the XYZ space. • Given the scaled RGB color matching functons, the RGB tristmulus values for a color with a spectral power distributon I (λ) are given by: