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When talking about image formats, we have to talk about bits A bits is the smallest unit of data that computers work with, and the number of bits that a format can devote to each pixel determines the maximum number of colors the format can display Now it s time for some new terms: n Bit depth: This is the total number of bits a file format is capable of devoting to each pixel This stat determines the total number of colors that a format can display in a single image, which is our next term n Color depth: This is the total number of colors that a format can display in a single image For example, GIF files can display a total of 256 colors in a single image, therefore GIF files have a color depth of 256.

. . . . . . . . . .

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Pretty simple so far, yes A format s color depth is calculated this way: We begin by taking the number of states a bit has which is two (one state being on, the other being off) and raise that number to the power indicated by the format s bit depth GIF is an 8-bit format, meaning it can devote 8 bits of data to each pixel So, using our handy little equation, this makes its color depth 28 or 2x2x2x2x2x2x2x2 which totals 256 This is the first of GIF s strengths: A low color depth helps ensure a low file size GIF s next major strength is how it compresses file data GIF uses a compression method called LZW, after its creators Lempel, Ziv, and Welch If you ve been creating TIFF files for ages, then you ve already seen this option in the TIFF Options dialog box.

Now that you know something about Bridge, I want to show you how to use the Preferences dialog box to further modify the look and feel of the workspace. left. Take a look at a few of the more relevant preferences now. Feel free to explore the rest of them when you re done here.

When turning an image into a GIF file, this compression method exploits inefficiencies in the file s data structure, effectively removing unused space within the file Consequently, no additional information is removed from the image In techno-babble, LZW compression is called a lossless compression method because no image data is lost in the process..

Algorithm 42.2 (A posteriorierror-feedback filter) Let 0 << X 5 1 be a forgetting factor and define IIM = q-1diag{X-2, . . , X - ( M + l ) } . Consider a reference sequence { d ( j ) } and a regressor sequence { u M , ~ } with shift structure and of dimension 1 x M, say, U M , ~ [ u ( j ) . . u ( j - M 1) = For each i 2 0, the M - t h order a posteriori estimation error, ' ~ ( 2 ) = d ( i )- U M , ~ Z L I M , that results from the solution o f the regularized least-squares ~, problem:

N this appendix we briefly discuss a number of elementary concepts of probability theory that are of use for performance evaluation purposes. By no means is this appendix to cover all of probability theory; it is included as a refresher for those having For further the mathand statistics in the past.

FIGURE 12.7 To create severe sharpening effects, enter a CMV just large enough to compensate for the negative values in the matrix (first and second examples). To heighten the sharpening effect even further, shift the negative values closer to the perimeter of the matrix (last example).

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17.12, we present the percentage of time the server is making checkas well as the expected time the server is re-serving unchecked jobs plus the expected restart time (E[down]), again for scenarios 1