Last Time Course introduction Digital Images The difference between an image and a display Ways to get them Raster vs Vector Digital images as discrete representations of reality Human perception in deciding resolution and image depth Homework 1 due Sept 14 9 9 04 University of Wisconsin CS559 Spring 2004 Today Intensity perception Dynamic Range Gamma mapping Color Start preparing for the projects Programming Tutorial 1 9 9 04 University of Wisconsin CS559 Spring 2004 Intensity Perception Humans are actually tuned to the ratio of intensities not their absolute difference So going from a 50 to 100 Watt light bulb looks the same as going from 100 to 200 So if we only have 4 intensities between 0 and 1 we should choose to use 0 0 25 0 5 and 1 Most computer graphics ignores this giving poorer perceptible intensity resolution at low light levels and better resolution at high light levels It would use 0 0 33 0 66 and 1 9 9 04 University of Wisconsin CS559 Spring 2004 High range low res Image depth refers to the number of bits available but not how those bits map onto intensities We can use those bits to represent a large range at low resolution or a small range at high resolution Common display devices can only show a limited dynamic range so typically we fix the range at that of the display device and choose high resolution Low range high res Dynamic Range All possible intensities 9 9 04 University of Wisconsin CS559 Spring 2004 More Dynamic Range Real scenes have very high and very low intensities Humans can see contrast at very low and very high light levels Can t see all levels all the time use adaptation to adjust Still high range even at one adaptation level Film has low dynamic range 100 1 Monitors are even worse Many ways to deal with the problem Way beyond the scope of this course 9 9 04 University of Wisconsin CS559 Spring 2004 Display on a Monitor When images are created a linear mapping between pixels and intensity is assumed For example if you double the pixel value the displayed intensity should double Monitors however do not work that way For analog monitors the pixel value is converted to a voltage The voltage is used to control the intensity of the monitor pixels But the voltage to display intensity is not linear Similar problem with other monitors different causes The outcome A linear intensity scale in memory does not look linear on a monitor Even worse different monitors do different things 9 9 04 University of Wisconsin CS559 Spring 2004 Gamma Control The mapping from voltage to display is usually an exponential function I display I to monitor To correct the problem we pass the pixel values through a gamma function before converting them to the monitor I to monitor I 1 image This process is called gamma correction The parameter is controlled by the user It should be matched to a particular monitor Typical values are between 2 2 and 2 5 The mapping can be done in hardware or software 9 9 04 University of Wisconsin CS559 Spring 2004 Some Facts About Color So far we have only discussed intensities so called achromatic light shades of gray Accurate color reproduction is commercially valuable e g painting a house producing artwork On the order of 10 color names are widely recognized by English speakers other languages have fewer more but not much more E commerce has accentuated color reproduction issues as has the creation of digital libraries Color consistency is also important in user interfaces eg what you see on the monitor should match the printed version 9 9 04 University of Wisconsin CS559 Spring 2004 Light and Color The frequency of light determines its color Wavelength is related Energy also related 1 Describe incoming light by a spectrum Intensity of light at each frequency A graph of intensity vs frequency We care about wavelengths in the visible spectrum between the infra red 700nm and the ultra violet 400nm 9 9 04 University of Wisconsin CS559 Spring 2004 White Photons White Less Intense White grey Wavelength nm 400 500 600 700 Note that color and intensity are technically two different things However in common usage we use color to refer to both White grey black in terms of color You will have to use context to extract the meaning 9 9 04 University of Wisconsin CS559 Spring 2004 Photons Helium Neon Laser Wavelength nm 400 500 600 700 Lasers emit light at a single wavelength hence they appear colored in a very pure way 9 9 04 University of Wisconsin CS559 Spring 2004 Photons Normal Daylight Wavelength nm 400 500 600 700 The sky is blue so what should this look like 9 9 04 University of Wisconsin CS559 Spring 2004 Photons Normal Daylight Wavelength nm 400 500 600 700 Note the hump at short wavelengths the sky is blue Other bumps came from solar emission spectra and atmospheric adsorption 9 9 04 University of Wisconsin CS559 Spring 2004 Photons Tungsten Lightbulb Wavelength nm 400 500 600 700 Most light sources are not anywhere near white It is a major research effort to develop light sources with particular properties 9 9 04 University of Wisconsin CS559 Spring 2004 Emission vs Adsorption Emission is what light sources do Adsorption is what paints inks dyes etc do Emission produces light adsorption removes light We still talk about adsorption spectra but now is it the proportion of light that is removed at each frequency Note that adsorption depends on such things as the surface finish glossy matte and the substrate e g paper quality The following examples are qualitative at best 9 9 04 University of Wisconsin CS559 Spring 2004 Adsorption spectra Red Paint Wavelength nm 400 500 600 700 Red paint absorbs green and blue wavelengths and reflects red wavelengths resulting in you seeing a red appearance 9 9 04 University of Wisconsin CS559 Spring 2004 Representing Color Our task with digital images is to represent color You probably know that we use three channels R G and B We will see why this is perceptually sufficient for display and why it is computationally an approximation First how we measure color 9 9 04 University of Wisconsin CS559 Spring 2004 Sensors Any sensor is defined by its response to a frequency distribution Expressed as a graph of sensitivity vs wavelength For each unit of energy at the given wavelength how much voltage impulses whatever the sensor provides To compute the response take the integral E d k E is the incoming energy at the particular wavelength The integral multiplies the amount of energy at each wavelength by the sensitivity at
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