Digital Cameras Essay, Research Paper Overview Digital cameras capture images electronically and convert them into digital data that can be stored and manipulated by a computer.
Digital Cameras Essay, Research Paper
cameras capture images electronically and convert them into digital data
that can be stored and manipulated by a computer.
conventional cameras, digital cameras have a lens, aperture, and shutter,
but they don’t use film. When light passes through the lens it is focused
on a photo-sensitive electronic chip called a charged coupling device
(CCD). The CCD converts light impulses into electrical impulses (also
called analog signal forms). The signals are fed into a microprocessor
and transformed into digital information. This process is called digitization.
digital images do not yet match the quality of pictures produced on film,
they represent an enormously flexible medium. Photographers are no longer
limited by the physical properties of chemistry and optics. Computers
outfitted with the appropriate software can augment and transform images
in ways never before imagined.
The origins of digital cameras are intimately linked with
the evolution of television in the 1940s and 50s, and the development
of computer imaging by NASA in the
Before the advent of the video tape recorder (VTR), television
images were optically displayed on monitors and then filmed by motion
picture cameras. Because film and television technologies were essentially
incompatible, Kinescopes, or "kinnys" as they were called, produced
A breakthrough occurred in 1951 when Bing Crosby Laboratories
introduced the VTR, a technology specifically designed to record television
images. Television cameras convert light waves into electronic impulses,
and the VTR records these impulses onto magnetic tape. Perfected in 1956
by the Ampex Corporation, video tape
recording produced clear, crisp and nearly flawless images. The use of
VTRs soon revolutionized the television industry.
The next great leap forward happened in the early 1960s
as NASA geared up for the Apollo Lunar Exploration Program. As a precursor
to landing humans on the moon, NASA sent out a series of probes to map
the lunar surface. The Ranger missions relied on video cameras outfitted
with transmitters that broadcast analog signals. These weak transmissions
were plagued by interference from natural radio sources like the Sun.
Conventional television receivers could not transform them into coherent
Researchers at NASA’s Jet
Propulsion Laboratory (JPL) developed ways to "clean" and
enhance analog signals by processing them through computers. Signals were
analyzed by a computer and converted into numerical or digital information.
In this way, unwanted interference could be removed, while critical data
could be enhanced. By the time of the Ranger 7 mission, JPL was producing
crystal clear images of the moon’s surface. The age of digital imaging
Since that time, probes outfitted with digital imagers
have explored the boundaries of our solar system. The orbiting Hubble
telescope, a hybrid of optical and digital technology, maps the limits
of the known universe.
Here on earth, digital techniques gave rise to a host
of medical imaging devices, from improved X-ray imaging in the late 1960s,
to Magnetic Resonance Imaging and
Emission Tomography in the ’80s and ’90s.
Thousand Points of Light: How Digital Images Are Formed
Digital cameras come in several formats designed for the
specialized needs of photographers. They range from inexpensive snapshot
models to sophisticated scanner backs that fit on professional large format
film cameras. Regardless of their size or sophistication, all digital
cameras operate in much the same way.
All images we perceive are formed from optical light energy.
Even digital images created within a computer are eventually converted
into light energy that we can see. In order for a digital camera to store
an optical image, it must be converted into digital information.
A digital camera gathers light energy through a lens,
and focuses it on a CCD which converts it into electrical impulses. These
signals are fed into a microprocessor where they are sampled and transformed
into digital information. This numerical data is then stored, and usually
transferred later on to a computer where the image can be viewed and manipulated.
A black-and-white photograph is composed of a wide range
of tonal variations. Like the spectrum of natural light it represents,
the photo’s tones are continuous and unbroken. By contrast, a black-and-white
digital image consists of myriad points of light sampled from the light
spectrum. A digital image’s range of tone is determined by the camera’s
capacity to sample and store different light values.
After the CCD converts light into an electrical signal,
it is sent to the image digitizer. The digitizer samples areas of light
and shadow from across the image, breaking them into points—or pixels.
The pixels are next quantized—assigned digital brightness values.
For black-and-white, this means placing the pixel on a numerical scale
that ranges from pure white to pure black. In color imaging, the process
includes scales for color resolution and chromatic intensity.
Each pixel is assigned an x,y coordinate that corresponds to its place
and value in the optical image. The more pixels, the greater the image’s
range of tone. This quality is called spatial density, and is a
vital component of image quality. How good a picture looks is also affected
by optical resolution—meaning the camera’s optics and electronics.
Together, spatial density and optical resolution determine the image’s
spatial resolution; its tonal spectrum and clarity of detail.
In the end, spatial resolution is decided by the camera’s lesser most
quality: spatial density or optical resolution.
If crisp, clear pictures are the result of spatial density,
then a camera’s digitizer should sample an image as broadly and often
as possible. The digitizer’s ability to do this results in the image’s
Imagine a picture of a palm tree on a sandy beach. The
sky is bright blue with barely a cloud in the sky. The sand is golden,
and covered here and there by white breakers. The ocean is an unbroken
expanse of deep blue. The palm’s dark forest greens are broken by shafts
of filtered light.
When the digitizer scans this image it will find the sky,
beach and ocean fairly simple patterns of continuous tones. They vary
little in brightness or color; one sampled point of light is pretty much
the same as the next one. These areas have low spatial frequency.
The digitizer doesn’t need many samples to accurately read their tones.
The tree, however, with its deep shadows and brilliant
highlights, presents a greater challenge. Bright tones and dark tones
vary greatly from one pixel to the next. This rapid rate of tonal shifting
is called high spatial frequency. In order to build an accurate
representation, the digitizer needs many more samples than it does for
a low frequency area.
After determining the area of highest spatial frequency,
the digitizer calculates a sampling rate for the entire image. That speed
is double the rate of the image’s highest spatial frequency. In this way
the digitizer captures all of the scene’s subtle tonal nuance.
Of course, the camera’s sampling rate is not infinite,
especially in lower priced models. It’s ability to sample is limited by
its number of pixels. Pixel density depends on the amount of capacitors
on the CCD chip. This varies quite a bit between different makes and models
of cameras. Generally, cameras are assigned spatial frequency rates that
cover most situations photographers are likely to encounter.
The apparent brightness of an object in the real world
is quite different from its representation in a picture. Anyone who has
ever gazed at the sun instinctively knows the difference between the actual
object and a photograph of it. This may seem an academic distinction,
but it is a key concept in digital imaging.
The sun, the moon, the trees and flowers—everything
we see in our physical environment—possess radiant intensity.
They emit and reflect light energy. Paintings, photographs, and digital
images, on the other hand, possess luminous brightness. Though
they have radiant intensity, it is not the same intensity as the objects
they represent. The sun shown on a television or movie screen does not
have the radiant intensity of the actual celestial body. It is a representation.
In a digital photograph, each pixel has an assigned brightness
value—a luminous brightness—that corresponds to a radiant intensity
in the physical world. This value is determined by how many bits are in
A 3-bit quantizer, for example, can only render a scale
of eight distinct tones ranging from pure white to pure black. If this
camera took a picture of our beach scene, it would create a high contrast
image with very few middle tones. This effect is called brightness
contouring, and is similar to the phenomenon of posterization in conventional
Brightness contouring has many pragmatic and creative
applications when an image is ready to be manipulated in a computer. However,
when capturing images with a camera, it’s best to preserve as wide a tonal
range as possible. Every bit added to a quantizer doubles its scale of
tones. Most modern digital cameras are equipped with 8-bit quantizers
capable of producing 256 different shades. Some professional quality cameras
have quantizers that can render well over a thousand tones.
Making digital images in color requires an additional
step. In black-and-white, the brightness resolution of a pixel is determined
by one gray value. In color, that value has three components, one for
each primary color, red, green or blue. This concept is called trichromacy.
Color digital cameras are outfitted with three different
sensors, each one sensitive to a primary waveband of light. After an image
is scanned and quantized, it is further broken down into color values.
Each pixel is assigned three color values which represent qualities of
red, green or blue. Color values are further distinguished by their hue
saturation and brightness.
Suppose, for example, a photographer snaps an image of
a pink balloon. The camera’s red sensor is stimulated and the quantizer
assigns the pixels that hue. Next, a saturation value is determined. Deep
red is a fully saturated color, while pink is much less saturated. It
is relatively faded and much closer to the white extreme of the scale.
Lastly, the brightness value determines the luminous intensity of the
color. Is this a pink balloon drifting through the shade of a forest?
Or does it float freely across a bright blue sky? These considerations
will compose the saturation and intensity of the image.
Imaging: From Camera to Computer
Most digital images form within a blink of the camera’s
shutter. In that fragmentary instant, an image made of light is transformed
into a stream of numerical data by a complex web of technologies. What’s
more, the image stored within the camera’s memory chip is only the beginning.
To be viewed and appreciated, the camera’s data must be uploaded into
a computer. Here, an imaginative photographer can alter and transform
the image in almost any way desired. With the proper software, even the
most mundane snapshot can evolve into a work of artistry.
The political, social and artistic ramifications of digital
imaging technology are yet to be ascertained. One thing is certain: the
way we create and perceive the fruits of human imagination will never
be the same.
Baxes, Gregory, Digital Image Processing: Principles &
Applications, New York: John Wiley & Sons, Inc., 1994.
Brown, Les, Les Brown’s Encyclopedia of Television: Third
Edition, Detroit: Gale Research, 1992.
Grotta, Sally Wiener, and Grotta, Daniel, Digital Imaging
for Visual Artists, New York: Windcrest/McGraw-Hill, 1994.
Katz, Ephraim, The Film Encyclopedia:Second Edition, New
York: Harper Perennial, 1994.
Baig, Edward C., "Smile—You’re on Candid Computer,"
Business Week, 4 November 1996.
Diehl, Stanford, "Byte’s Video Workshop," Byte,
Joch, Alan, "Beyond Hollywood," Byte, May 1995.
Lu, Cary, "Digital Cameras on the Move," MacWorld,
McNamara, Michael J.,"New Imaging, Today & Tomorrow:
3 New Digital Cameras," Popular Photography, August 1996.
Wiener, Leonard, "Camcorders Go Pro," U.S. News
& World Report, 25 November 1996.
Zuckerman, Jim, "Digital Portraits," Petersen’s
Photographic, September 1996.
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