Processing and Managing Images in Institutions of Higher Education

Abstract

Recent improvements in digital cameras and scanners have caused these tools to become popular in educational environments. As a result, more and more faculty and staff members are engaged in processing digital images to prepare teaching materials and conduct research. However, they often struggle to organize large numbers of digital images. They also look for efficient methods of editing images, so that they can produce different versions of an image for the Web, email, and printing.

This paper will address strategies for processing images both individually and in batches, as well as techniques for organizing images using inexpensive software. Many of the strategies discussed will work in all three major operating environments: Windows, Unix, and Macintosh. Some tools specific to each platform will also be covered.

Introduction

In recent years, four factors have led to the widespread use of digital images: First, those technologies which make it easier to disseminate images, in particular the World Wide Web and email. Secondly, a drop in the cost of hardware, such as scanners and digital cameras which are used to create digital images. Thirdly, inexpensive hard disks for storage and devices for reading and writing CD-ROMs. Lastly, the low cost of outputting images on a computer display as compared to the high cost of full color printing.

Universities have followed this trend toward a greater use of digital images. Faculty use images as building blocks of course content and for disseminating research data. College administrators utilize images to advertise scholastic programs to attract potential students. Further, campus documents that used to only consist of text are now being reborn on the Web filled with images. Even textual information is often conveyed through digital images because HTML currently lacks strong tools for controlling the appearance and layout of text.

This widespread use of images has created a great demand for tools for their processing and management. A variety of applications that can run on Windows, Macintosh, and Unix computers have been written and are continually being refined as users demand more features. Since these software programs are constantly being enhanced and image-related technologies, such as Web browser software and HTML standards, are rapidly evolving, it is difficult to determine which type of image-processing software is the best for the task at hand.

To alleviate this difficulty, we need to understand digital images in general and learn how to evaluate various image-processing software in particular. This paper will focus on identifying basic characteristics of digital images, introducing software that are available in the market for managing and processing images, and provide criteria for evaluating and selecting these programs.


Basic Characteristics of Digital Imaging

Digital images can be categorized as either vector images or bitmapped images. Created by drawing programs, vector images are generally used to represent lines, polygons, and curves and serve in initial steps when using computer assisted design (CAD) software. In contrast, bitmapped images are used not only to display simple lines and shapes as vector images, but are also used for photographic images, realistic paintings, and other complex subjects. Because of their ability to represent these complex subjects, bitmaps are commonly used for depicting photographic and naturalistic images for use both on the World Wide Web and on paper.

A bitmapped image can be thought of as a group of data points which are used to represent colors and their intensity in the subject. According to Murray & VanRyper (1994, p.8), ABitmap data is formed from a set of numerical values specifying the colors of individual pixels or picture element. Pixels are dots of color arranged on a regular grid in a pattern representing the form to be displayed.@ The number of data points assigned to the image determines the amount and sharpness of detail which can be discerned by a viewer. Milburn refers to this characteristic of a digital image as its resolution. (2000, p.17) The resolution of an image is generally expressed in terms of the physical dimensions of an image given as the width by the height in pixels.

Resolution is also used to describe devices used in the creation or display of images. For example, a SuperVGA monitor is capable of displaying an image which is 800 by 600 pixels wide. Sometimes we also describe monitors in terms of Adots per inch.@ which can be thought of as pixels per inch. A typical desktop monitor is capable of displaying only 72 - 85 dots per inch. Current IBM compatible computers are often sold with their resolution set to 800 x 600 or 1024 x 768. These setting can be changed using the Windows operating system, but their maximum possible resolution is rarely over 1600 x 1200, except for extremely high end graphics workstations used by imaging professionals. These numbers are roughly the same for Macintosh computers as well. Expensive Unix workstations tend to be at the high end of this monitor resolution scale.



The resolution of a digital camera is usually described by multiplying the height by the width in pixels of the image that the camera can produce. A camera which can produce an image which is 2000 by 1500 is called 搕hree megapixel@ camera. The resolution of some devices, such as scanners and printers, may also be given as the number of pixels or dots in a given unit of measurement.


Making practical use of printer resolution is a bit more complex than for some other devices, because printers combine primary colors to simulate the colors in your image. As a rule of thumb, the printer resolution can be divided by three (corresponding to the three RGB colors) in order to determine the maximum practical resolution for printing. Many printer manufacturers

use two numbers to describe a printer抯 resolution, one for the vertical scanning and the other for horizontal. You can use both of these numbers to get a range of the dpi which can be used for high definition printing. For example, a printer with a resolution of 1200 x 600 will print a high definition image at 400 to 200 dpi. At 400 dpi, the image will be printed smaller and sharper. For many modern color printers, experts recommend 300 dpi as being a reasonable number (Milburn, 2000, p. 31).

The resolution of a file can be changed up or down by resizing the image. However, if the size of an image is increased or scaled up, the space between the original pixels must be filled in. The software used to make the file larger will fill in the space between the original pixels, however the result is often very fuzzy looking. Newer imaging software uses the process of interpolation which is a more intelligent way of choosing the pixels which are used as filler, but in general images which are scaled up more than ten or twenty percent lose much of their sharpness. Higher end imaging software programs use more sophisticated interpolation algorithms and tend to produce better results. Reducing the resolution of an image can generally be done to a much greater extent with no noticeable loss of quality. In order to reduce the resolution of an image, the imaging software must throw away pixels. Images can often be scaled down by more than fifty percent and still be of acceptable quality.

Resolution describes the amount of detail in an image, but the tonal quality of each pixel must also be considered. The pixel depth, also known as bit depth of an image, controls its tonal qualities. Milburn (2000, p.21) describes Apixel depth@ as bits per pixel because the amount of color information which can be assigned to each pixel depends on the amount of data that can be assigned to that pixel. A 1-bit image can only display black and white. To