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True Color versus Palette The examples in this book assume that the output device uses the RGB color model to display images and that each component is represented using 8 bits and is in the range 0-2553 This is the color representation used by most personal computer systems Such a system can produce 16,777,216 (2563) distinct colors There are computer systems that use more bits to represent color, for example the 12-bit grayscale frequently used for medical images Some image formats support data with more than 8 bits per component (12 for JPEG, 16 for PNG) For the remainder of this discussion we are going to assume that you are working on a system that uses 8 bits per component Two methods are commonly used to assign one of the possible colors to a pixel The simplest is to store the color value for each pixel in the compressed data For images with 24 bits per pixel, each pixel has a 3-byte color value associated with it Images that use 24 bits or more are called true color because over the range of colors a monitor can display, 24 bits per pixel is the limit of color differences that a human can distinguish The problem with 24-bit graphics is that while a system may be capable of displaying 16,777,216 different colors, it may not be able to do so simultaneously Older computers may not even have a video card capable of using a 24-bit display mode Newer computers may not have enough video memory to operate in 24-bit mode at higher screen resolutions A display on a personal computer set at a resolution of 1024 768 pixels would require 2,359,296 (1024 768 3 = 225 MB) of video memory to display 24-bit images If the computer had only 2 MB of video memory it could not display 24-bit images at this resolution but could do so at a lower resolution of 800 x 600 (800 600 3 = 14 MB) The solution devised to represent colors before the days of displays capable of 24 bits per pixel was to define a color palette that selects a subset of the possible colors Conceptually the palette is a 1-dimensional array of 3-byte elements that specify the color Rather than directly specifying the color, each pixel value is an index into the color palette The most common size for a palette is 256 entries where each pixel value consists f 8 bits Most computers today can.

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smallest possible number of bits (almost always 9) When the number of codes becomes too large to be represented by the current code size, the code size is increased by 1 bit If the initial code size is 9, codes are output using 9 bits until code 512 is created Then the code size is automatically bumped up to 10 bits Likewise, after reaching 1024 it is increased to 11 bits Twelve bits is the maximum code size allowed by GIF When the code values reach 212-1, GIF encoders and decoders stop adding to the dictionary A GIF encoder can output a special clear code to instruct the decoder to reset the dictionary to its initial state An encoder may wish to output a clear code whenever it detects a situation where compression would be improved by using a new dictionary An obvious situation where this might be desirable is after the maximum code sized is reached Repeated clear codes are legal, but wasteful The value for the clear code is not fixed, but rather depends upon the minimum code size that followed the image header The minimum code size gives the number of bits used to encode the pixel data Usually this value is 8 bits per pixel, but pixel values for mages that do not use 256 colors can be compressed using fewer bits Table 1211 shows the relationship of the minimum code size to the code usage within a GIF image The end code is used to mark the end of a compressed stream Dictionary Structure In the GIF compression and decompression examples we used an array of strings to represent the dictionary Normally the dictionary is represented as a tree structure Figure 127 shows how the strings generated from compressing the string ABABCABD can be arranged in a tree structure A code can be translated to a string by starting at the tree node for the code and working to the tree root Translating the code by walking the tree from a leaf to the root produces the string in reverse order In Figure 127, following the code 260 gives "DBA" A stack is used to put the string in the correct order While walking the tree, each time a leaf node is reached, the corresponding character is pushed on the stack When you reach the root of the tree, you pop characters off in the correct order The tree can be represented as an array of structures Since the maximum code length is 12 bits, the maximum number of tree nodes is 212 The size of the stack is also 212 characters.

The mean and standard deviation for the number of admissions per employee were 712 and 274, respectively The mean and standard deviation for number of beds per employee were 029 and 014, respectively The higher standard deviation of number of admissions per employee compared to the number of beds per employee shows that some hospitals have lower admissions per employee compared to other hospitals The differences in hospitals in terms of number of beds er employee are relatively low Among the set of factors that lead to the demand of human resources are the admissions, physical capacity (beds in our case), special ambulatory services, type of hospital, and the top DRG service In other words, the employee requirement may be represented in the following functional form: Number of employees f admissions; number of beds; number of emergency service visits; hospital ownership; top DRG where f() maps the independent variables to the dependent variable The total number of employees is a monotonic function of admissions, number of beds, and number of emergency service visits The hospital ownership and top DRG are binary variables that were operationalized as 0 and 1 For the not-for-pro t hospitals the value of 1 was used and for pro t hospitals a value of 0 was used In the case of top DRG, when top DRG was 127 (about 80% of the time) a value of 1 was used for input; a value of 0 was used for input when the top DRG was not 127 Since there are only three independent variables that take continuous values, and the dependent variable is considered to have a monotonic relationship with these three independent variables, four variables are used to compute technical ef ciency using DEA These four variables provide a one output (number of employees) and three inputs (admissions, number of beds, and number of emergency visits) combination for computing ef ciency using DEA [13] In our ANN analysis, 175 hospitals were selected from 188 hospitals Thirteen hospitals were discarded since the top DRG in these hospitals was different from the top DRGs listed in Figure 36 All the selected 175 hospitals contained one of the top ve DRGs identi ed in Figure 36 as the major DRG We conducted two different experiments Our rst experiment consisted of randomly dividing the data into two sets The rst set, consisting of 100 hospitals, was used to train the neural network, while the second, comprising 75 hospitals, was used to test the performance of the trained neural network In our second experiment, we took the rst set of 100 hospitals and computed the relative ef ciency of each hospital using DEA The of 100 hospitals was then divided into two sets of 50 hospitals The rst set of 50 hospitals was called ef cient and the other set of hospitals was called inef cient set The ef cient set contained the top 50 highest-ranking hospitals based on their DEA ef ciency score It has been suggested that training data for nonparametric models should be at least 10 times the number of input variables Since we have ve inputs, a minimum of.

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We will use the C5510 DSK for experiments and MATLAB functions available in the Image Processing Toolbox for image rocessing, analysis, visualization, and algorithm development in visual c#net generate, create barcode none with Each pixel of color images can be represented sing three numbers for the three primary colors: red (R), green (G), and blue (B), thus is often referred as RGB data Proper mixing of these three primary colors can generate different colors Each data can be stored as a byte in memory, and this forms the commonly used 24-bit RGB data (8 bits for R, 8 bits for G, and 8 bits for B) Color images are widely used in photographs, television broadcasts, and computer displays The 1-D signal processing is constrained by the sampling theorem, while the digital image is bonded in spatial domain The image resolution is the ability of distinguishing spatial details of images.

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