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.

Like

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.

Although

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.

History

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

1960s.

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

inferior images.

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

images.

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

had dawned.

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

Positron

Emission Tomography in the ’80s and ’90s.

A

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.

Black-and-White

Basics

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.

Spatial

Resolution:

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.

Spatial

Frequency

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

spatial frequency.

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.

Brightness

Resolution

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

the quantizer.

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

photography.

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.

Color

Resolution

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.

Digital

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.

Bibliography

Books

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.

Articles

Baig, Edward C., "Smile—You’re on Candid Computer,"

Business Week, 4 November 1996.

Diehl, Stanford, "Byte’s Video Workshop," Byte,

May 1995.

Joch, Alan, "Beyond Hollywood," Byte, May 1995.

Lu, Cary, "Digital Cameras on the Move," MacWorld,

June 1996.

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.