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IMPROCES(C). Copyright John Wagner 1991. All rights reserved. An Image Processing and VGA Primer This article may only be distributed as part of the IMPROCES, Image Processing Software package by John Wagner. IMPROCES is used for the examples and it is assumed the reader has a copy of the program. This article may not be reproduced in any manner without prior permission from the author. The Image and The Screen: Images are represented as a series of points on a surface of varying intensity. For example, on a monochrome or black and white photograph, the points on the image are represented with varying shades of grey. On a computer screen, these points are called pixels. Pixels on the screen are mapped into a two dimensional coordinate system that starts at the top, left corner with the coordinate 0,0. The coordinates in the X direction refer to pixels going in the right (horizontal) direction and coordinates in the Y direction refer to pixels going in the down (vertical) direction. The coordinates X,Y are used to define a specific pixel on the screen. When a computer image is said to have a resolution of 320x200x256, it means that the image has a X width of 320 pixels, a Y length of 200 pixels and contains 256 colors. Pixel Mapping on 320x200 video screen: 0,0 319,0 X--------> Y | | V 0,199 319,199 Color: A shade of grey is defined as having equal levels of Red, Green and Blue (RGB). A color image however is represented as having points that are represented by varying levels of RGB. NOTE: The color model of RGB is just one way of representing color. Conveniently it is also the model that computers use when representing colors on a screen. For that reason, it will be the model we will use here! When splitting up a color into its RGB components, it is common to use a number between 0 and 1 (or percentage of total color) to represent the colors RGB intensities. R=0, G=0, B=0 (usually shown as 0,0,0) would be the absence of all color (black) and 1, 1, 1 would be full intensity for all colors or white. .5, 0, 0 would be half red, while 0, .25, 0 would be one quarter green. Using this model, an infinite amount of colors are possible. Unfortunately, personal computers of this day and age are not capable of handling an infinite amount of colors and must use approximations when dealing with color. The VGA: With the advent of the VGA video subsystem for IBM PC's and compatibles, Image Processing is now available to the home PC graphics enthusiast. Also, since this article is meant to be used with IMPROCES, it will help to understand the limitations of hardware we are working with. The VGA can display 256 colors at one time out of a possible 262,144. The number 262,144 is not a magical number. It comes from the limitations of the VGA hardware itself. The VGA (and many common graphics subsystems) represent their 256 colors in a look up table of RGB values so that the memory where the video display is mapped need only keep track of one number (the look up value) instead of three (see diagram 1). The look up table of the VGA allows for 64 (0 to 63) levels of RGB for each color. This means that 64 to the 3rd power of different colors are possible. 64^3=262,144. Because only 64 levels of RGB are possible, it is only possible to represent 64 shades of grey with a VGA. Remember that a shade of grey is defined as equal levels of RGB. There is also a disparity in the common usage of values from 0 to 1 to define a level of RGB. However, this problem is easily solved by dividing the VGA look up table RGB number by 64 to get its proper percentage of the total color (exp. 32/64=.5 or 50% of total color). Diagram 1. Sample Color Look Up Table (LUT) for VGA Color R G B 1 0 23 56 2 34 24 45 3 23 12 43 .... 254 13 32 43 255 12 63 12 Another important aspect of the VGA is that it not only allows up to 256 colors to displayed at one time. There are now Super VGA cards that allow you to work in display resolutions up to 1024x768 with 256 colors. These Super VGA cards have more memory on board so they can handle the higher resolutions. It is important to understand that the amount of memory on the video board will determine the highest resolution it can handle. Video Memory is bitmapped, in a 256 color mode, one pixel requires one byte of memory as a byte can hold a value from 0 to 255. VGA video memory begins at the hexidecimal address A0000000. The VGA video memory area is 64K in length. Because of the length of the VGA memory area, the maximum resolution a standard VGA card can achieve is 320x200x256. This is because 320 pixels (bytes) times 200 pixels (bytes) equals 62,000 bytes (62.5K) and fills the video memory area. pixel 0,0───┐ 0,1 0,2 │ │ │ Memory Address A0000000 ┘ │ │ A0000001 ───┘ │ A0000002 ────────┘ But wait a second, how can we get video modes up to 1024x768? If you want a resololution of 1024x768x1byte(256 colors), you require 1024x768 bytes of video memory, 786,432 bytes, or 768K. Because the architecture of the PC only allows for a maximum of 64K of VGA memory, a Super VGA card maintains its own pool of memory that is displayed to the screen and swaps memory in and out of the 64K "proper" VGA video memory address space so that programs can write to it. IMPROCES Stuff: IMPROCES image processing functions all work in a defined area called the WORK AREA. The default work area starts at the top-left corner of the screen and ends at pixel 196 in the X (horizontal) direction and at pixel 165 in the Y (vertical) direction. These aren't magical numbers either. A friend of mine lent me a CCD device to capture greyscale images and process them with IMPROCES. The size of the image the CCD device output was, you guessed it, 196x165. You can change the WORK AREA by selecting WORK AREA from the ENHANCE pull down menu and then defining a new rectanglar area for IMPROCES to use. The Histogram: With all of that out of the way, lets examine a few things we can learn from an image without actually modifying it. A tool that is commonly used to determine the overall contrast of an image is the Histogram. A histogram is defined as the measure of the distribution of a defined set. As you probably know, histograms are not unique to image processing. The histogram takes a count of all the values on the image and displays them graphically. When I say a count, I mean how many pixels that contain the color in LUT 0 through 255. The count of a color is called that colors BIN. To get a histogram in IMPROCES, select AREA HISTO from the ENHANCE pull down menu. The histogram is displayed from the left to the right starting at color 0 and working over a column at a time to color 255. The BIN's are displayed as lines going upward. You can move the mouse to a desired BIN and click on that column to get that columns exact count, which is shown in the lower right corner. You can also press 'S' to save the histogram to an ASCII file that you can examine later. Assuming that the image is a greyscale image, the histogram shows us the overall contrast of the image by how much of the greyscale is covered by the image. A high contrast image will cover most of the greyscale while a low contrast image will only cover a small portion of the greyscale. Examples of Histograms: High Contrast Image: -100 x6 │││ - │ ││││ - ││││││││ - │││││││││ - │││││││││││ - └┴┴┴┴┴┴┴┴┴┘ -0 0 255 Low Contrast Image: -100 x8 │││ - ││││ - ││││ - ││││ - ││││ - ────┴┴┴┴─── -0 0 255 Contrast Enhancement: Contrast enhancement is one of the easiest to understand of the image processing functions. As of version 3.0 of IMPROCES, contrast stretching will only work properly on images with a greyscale palette. Future versions of IMPROCES will probably allow for contrast stretching of color images. Contrast Stretching will take a portion of the greyscale and stretch it so that it covers a wider portion of the greyscale. To do this, you must first define an area of the greyscale that you would like to stretch. IMPROCES provides three ways to do this. All of the methods use two variables, one called Low_CLIP (L_CLIP) and one called the High_CLIP (H_CLIP). Depending on which method you use, the variables will be used in different ways. When using the CNTR STRTCH method, the first BIN working up from 0 that contains more pixels then the value of L_CLIP will become the color 0 (black), any BINS below that value are set to 0 as well. The first BIN working down from 255 that contains more pixels then the value of H_CLIP will become the value 255 (white) and any BINS above that will become 255 as well. All of the BINS in between will be remapped between 0 and 255 by a ratio of where they where in respect to the original LOW and HIGH CLIP values. Take the original low contrast image: Low Contrast Image: -100 x8 │││ - ││││ - ││││ - ││││ - ││││ - ────┴┴┴┴─── -0 0 255 L_CLIP and H_CLIP are both set to 30 so the L_CLIP and H_CLIP will hit at these points: -100 x8 │││ - ││││ - ││││ - ││││ - ││││ - ────┴┴┴┴─── -0 0 | | 255 L_CLIP H_CLIP These BINS will now be reset to 0 and 255 and the BINS in between are set in respect to there original location to the L_CLIP and H_CLIP BINS: Constrast Stretched Image: -100 x8 │ │ │ - │ │ │ │ - │ │ │ │ - │ │ │ │ - │ │ │ │ - └──┴───┴──┘ -0 0 255 L_CLIP H_CLIP The resulting image will have its contrast stretched across the entire greyscale, resulting in a higher contrast image. The standard CNTR STRCH works well for a lot of images. The problem is, rarely will an image be spread so evenly across the greyscale to begin with. A lot of times there will be spikes in the histogram at either end that you might want to remove. Histogram with spikes: -100 x6 │ │ │ - ││ │││ │ - ││ ││││ │ - │││││││││ - │││││││││ - ─┴┴┴┴┴┴┴┴┴─ -0 0 255 Using the standard contrast stretch, you would not be able to get over these spikes if you wanted to stretch the middle of the histogram. IMPROCES provides the CNTR VSTCH for this purpose. This method uses the variables L_CLIP and H_CLIP to pick which BIN you want to be the L_CLIP and H_CLIP values, without regard to their values. The only thing to remember is not to set the L_CLIP higher then the H_CLIP, otherwise the program will give you an error message. Besides the difference in the way the program uses the variables, VSTCH works identical to STRCTH. CNTR LSTRCH works the same as VSTCH in the respect that the variables are used to pick the L_CLIP and H_CLIP values. The difference is that the BINS that are below the L_CLIP are not set to 0, they are left alone and the same for the BINS above the H_CLIP value are left alone, not set to 255. Only the BINS in between L_CLIP and H_CLIP are stretched between 0 and 255. Convolution: Convolution is a another common method of processing an image. It uses a pixels neighbors as a function of its new value. The main points of convolution can be explained rather easily: A matrix is decided upon that contains certain values. This matrix is called a Kernel. The kernel is then passed over the image from left to right, top to bottom, with the value of the center pixel being replaced with the sum of the product of the kernel values and the pixels under them. A kernel with an odd number for its width and length is used. IMPROCES uses a 3x3 kernel: ┌───┬───┬───┐ ├───┼───┼───┤ ├───┼───┼───┤ └───┴───┴───┘ An odd length and width is used so that there will be center point on the kernel. Values are then assigned to the kernel: Sharpening kernel: -1 -1 -1 -1 9 -1 -1 -1 -1 The kernel is then passed over the image from left to right, top to bottom. The kernal is then multiplied, point by point with the with pixels in the 3x3 section of the image under it. The products are then summed and the middle pixel in the image under the kernal is then replaced with this new value. Sharpening kernel: -1 -1 -1 -1 9 -1 -1 -1 -1 Area of image being processed: 23 34 25 23 43 21 23 43 43 Product of values: -23 -34 -25 -23 387 -21 -23 -43 -43 Sum of products: 156 Convoled Area of image: 23 34 25 23 156 21 23 43 43 The new value of the center pixel is then written to the screen. You should note that the output from the previous operation is not used for input in the next operation. The input and output image must be treated separately. IMPROCES does this operation in place by using two rotating three line buffers. The resulting image is said to have convoled from the original. The example shows a sharpening kernel. A sharpening kernel is actually a Laplacian kernel with the original image added back in. The laplacian is an edge detecting kernal, so when you detect the edges of the image and then add the original image back in, you sharpen the edges, thereby improving the sharpness of the image. A laplacian kernel looks like so: Laplacian kernel: -1 -1 -1 -1 8 -1 -1 -1 -1 You will notice that the center value for the laplacian is an 8 and the sum of the kernel is 0. What this does to an image is make areas that have no real features and are close to being a continous tone disappear and leave features that have a lot of contrast with their neighbors. This is the function of an edge detector. If the kernal has a sum of 0, it will enhance the edges of an image in a certain direction. In the laplacians case, the enhancement will take place in all directions. Here is why this will happen: If you have a 3x3 area of the image that all the pixels are equal to the same same value, say all of the pixels are equal to 200, it will be uniform in color and contain no edges. When the laplacian is passed over this section, the result of the convolution will be 0, or the absence of an edge. If you have an area the looks like: 100 100 100 200 200 200 250 250 250 The result of the convolution with the laplacian would be 150, and there would be an edge present. Take for example a run of pixels that looks like this: 0 150 200 That area obviously contains an edge. On a graph it would look like this: /|255 / |200 / |150 0-----| If you used a 1x3 kernal with the values -1 0 1, the middle value would become 200 ( (0x0=0) + (150x0=0) + (200x1=200)) and show the precence of the edge. If the values of the run were 100 100 100, the area would be uniform and the middle pixels value would become 0 and accurately depict the uniform area. A horizontal kernel looks like this: Horizontal kernel: -1 -1 -1 0 0 0 1 1 1 You will notice that the horizontal kernal will only detect edges in the horizontal direction due to the direction of the of the kernal. A vertical kernel looks like this: Vertical kernel: -1 0 1 -1 0 1 -1 0 1 The same goes for the vertical kernal, only the direction is in the vertical direction. There are other methods of edge detecion available that IMPROCES does not implement at the present time. In fact there are new methods and filters being invented everyday. Future versions of IMPROCES will support convoling an area with a seperate filter for each direction and other forms of edge detection. There is also a variable called BOOST that is used when using the convolution filters. BOOST is used to increase or decrease the amount of the filter that is applied to the original image. What happens is the pixels under the kernel are first multiplied by the corresponding kernel value and then are multiplied by the BOOST value. A BOOST value of less then 1.0 will lessen the effect the filter has, while a value greater then 1.0 will increase the effect. IMPROCES includes a Custom Filter function that lets you define the kernel to use. You will also note that as of version 3.0 of IMPROCES, there are separate functions for greyscale images and for color images. The greyscale functions work a lot faster. The color functions must convol the RGB attributes of each pixel and then search the palette for the proper color to replace the pixel with. The color process rarely will find an exact match for the color that gets produced from the convolution, but it will find the closest possible match and use it. The grey functions need only convol the LUT value of the pixels and use the result to get an exact match. Average and Median: Two other filters that are included are the Average and Median filters. Both of these use a 3x3 matrix as before, but only the values in the image are used. Average will find the average value of the pixels under the matrix and replace the center pixel with that value. Median will find the middle value used and use that. Both functions work the same for greyscale and color images. An important note for all of the functions that use a matrix or a kernel is that the pixels on the edges (top, right, left, bottom) of the work area will not be affected by the functions. This is because these pixels do not have enough neighbors to be used in processing. In IMPROCES these pixels are simply left alone. Wrap it up!: All in all, image processing is used in many fields; Astronomy, dentistry, forensic science and X-Ray to just name a few. Software with a price of $25 like IMPROCES and the price of personal computers equipped with VGA and SVGA hardware dropping like a rock, have made image processing available to the masses. It is exciting, useful and most of all fun. I hope this little primer has peeked your interest in image processing and that it helps make some things that IMPROCES can do a little clearer. If for some reason you obtained this article without a copy of IMPROCES, the latest version of the program can be downloaded from the Dust Devil BBS in Las Vegas, Nevada, (702)796-7134. I can also be reached at the Dust Devil BBS if you have any questions about IMPROCES.