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Issue 6.5

FEATURE

REALGraphics

Working With Histograms

Issue: 6.5 (July/August 2008)
Author: JC Cruz
Author Bio: JC is a freelance technical writer living in British Columbia. He writes for various publications, pokes around with Cocoa, Python, and REALbasic, and spends time with his nephew. He can be reached at: anarakisware@gmail.com
Article Description: No description available.
Article Length (in bytes): 53,227
Starting Page Number: 21
Article Number: 6510
Resource File(s):

Download Icon 6510.zip Updated: 2013-03-11 19:08:00

Related Web Link(s):

http://en.wikipedia.org/wiki/SMPTE_color_bars
http://apollo.sese.asu.edu
http://marsrovers.jpl.nasa.gov/home/index.html
http://www-128.ibm.com/developerworks/rational/library/content/RationalEdge/sep04/bell/
http://homepages.inf.ed.ac.uk/rbf/HIPR2/histeq.htm
http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
http://en.wikipedia.org/wiki/Image_histogram
http://en.wikipedia.org/wiki/Histogram_equalization

Excerpt of article text...

In today's REALGraphics article, we will learn the basics behind an image histogram. First, we study the concept of a histogram and how it relates to image data. We then explore two ways of modifying the histogram data. Next, we build a set of REALbasic classes that will build and manage an image's histogram data. And then we will use these classes to analyze a handful of test images.

The Image Histogram

We all know that all digital images are comprised of a series of dots called pixels (short for "picture elements"). We also know that pixel depth determines the number of colors it can display. For instance, pixels in a black and white have a depth that is exactly 1 bit, where 1 is white, and 0 is black. The pixels in a CGA image, which has only 4 colors, will have a depth of 2 bits. Most modern images, such as those handled by REALbasic, use pixels that are 24-bits in depth. As a result, these pixels can display up to 16777216 colors, sufficient for most photo-quality work.

But pixel count or depth are not the only characteristics that affect image quality; pixel distribution is a factor as well. One way to look at pixel distribution is to use an image histogram.

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