Last TimeTodayIdeal ImagesDigital ImagesDigital CamerasPhotographyDiscretization IssuesPerceptual IssuesIntensity PerceptionDynamic RangeMore Dynamic RangeDisplay on a MonitorGamma ControlSome Facts About ColorLight and ColorWhiteHelium Neon LaserNormal DaylightTungsten LightbulbAdsorption spectra: Red PaintKey Concepts1/22/04 © University of Wisconsin, CS559 Spring 2004Last Time•Course introduction•Image basics1/22/04 © University of Wisconsin, CS559 Spring 2004Today•More on digital images•Introduction to color•Homework 1•Programming pre-project 11/22/04 © University of Wisconsin, CS559 Spring 2004Ideal Images•The information stored in images is often continuous in nature•For example, consider the ideal photograph:–It captures the intensity of light at a particular set of points coming from a particular set of directions (it’s called irradiance)–The intensity of light captured by a photograph can be any positive real number, and it mostly varies smoothly over space–Where do you see spatial discontinuities in a photograph?FilmFocal point1/22/04 © University of Wisconsin, CS559 Spring 2004Digital Images•Computers work with discrete pieces of information•How do we digitize a continuous image?–Break the continuous space into small areas, pixels–Use a single value for each pixel - the pixel value (no color, yet)–No longer continuous in space or intensity•This process is fraught with danger, as we shall seeContinuousDiscretePixels: Picture Elements1/22/04 © University of Wisconsin, CS559 Spring 2004Digital Cameras•CCD stores a charge each time a photon hits it–“Bins” have discrete area, one per pixel–Spatially discrete•Camera “reads” the charges out of the bins at some frequency•Convert charges to discrete value–Discrete in intensity•Store values in memory - the imageLight inLensCCD1/22/04 © University of Wisconsin, CS559 Spring 2004Photography•Can you make an arbitrarily large print of a digital image?•Hence, does it record continuous information accurately?–Resolution determines how much information is recorded•Can you take a photograph of a really bright thing?•Can you take a photograph of a really dark thing?•Can you take a photograph with light and dark things at the same time?–The ratio of the brightest thing to the darkest thing you can capture is called dynamic range1/22/04 © University of Wisconsin, CS559 Spring 2004Discretization Issues•Can only store a finite number of pixels–Choose your target physical image size, choose your resolution (pixels per inch, or dots per inch, dpi), determine width/height necessary–Storage space goes up with square of resolution•600dpi has 4× more pixels than 300dpi•Can only store a finite range of intensity values–Typically referred to as depth - number of bits per pixel•Directly related to the number of colors available and typically little choice•Most common depth is 8, but can also get 16 for grey–Also concerned with the minimum and maximum intensity – dynamic range•The big question is: What is enough resolution and enough depth?1/22/04 © University of Wisconsin, CS559 Spring 2004Perceptual Issues•Spatially, humans can discriminate about ½ a minute of arc–At fovea, so only in center of view, 20/20 vision–At 1m, about 0.2mm (“Dot Pitch” of monitors)–Sometimes limits the required number of pixels•Humans can discriminate about 8 bits of intensity–“Just Noticeable Difference” experiments–Limits the required depth for typical dynamic ranges–Actually, it’s 9 bits, but 8 is far more convenient•BUT, while perception can guide resolution requirements for display, when manipulating images much higher resolution may be required1/22/04 © University of Wisconsin, CS559 Spring 2004Intensity 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 11/22/04 © University of Wisconsin, CS559 Spring 2004Dynamic Range•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 resolutionAll possibleintensitiesLow range, high resHigh range, low res1/22/04 © University of Wisconsin, CS559 Spring 2004More 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 course1/22/04 © University of Wisconsin, CS559 Spring 2004Display 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–Same 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 things1/22/04 © University of Wisconsin, CS559 Spring 2004Gamma Control•The mapping from voltage to display is usually an exponential function:•To correct the problem, we pass the pixel values through a gamma function before converting them to the monitor•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 softwaremonitortodisplayII1imagemonitortoII 1/22/04 © University of Wisconsin, CS559 Spring 2004Some Facts About Color•So far we have only
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