Figure G3 Effect of JPEG compression from 0% to 100% with close-ups shown at right. in .NET Writer gs1 datamatrix barcode in .NET Figure G3 Effect of JPEG compression from 0% to 100% with close-ups shown at right.

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Appendix G using barcode drawer for vs .net control to generate, create datamatrix image in vs .net applications. Modified Plessey 75 100. Figure G3 Effect of JPEG compression from 0% to 100% with close-ups shown at right. Images With the development and pop ularity of digital photography, and the multiplication of Internet websites, the compression of images is playing an increasingly important role. Not only does it save memory space, but its also make it possible to speed up the downloading of web pages or email picture attachments, up to the point of instant grati cation. The most commonly used standard, JPEG, is the creation of the Joint Photographic Expert Group, which was launched in the mid 1980s under the ISO standardization body.

29 The JPEG standard, which is de ned through various lename extensions .jpg, .jpeg, .

jpe, .j f, and .jif, is a lossy compression codec.

This feature is illustrated in Fig. G3. The original le size corresponding to the top-left image (as converted for simplicity into a grayscale one) is 683 kbyte.

Compressing the same image through a photo editor by command factors of 50%, 75%, and 100% reduced the le size to 102 kbyte, 65 kbyte, and 15 kbyte, respectively. A look at the close-ups reveals that the effect of lossy compression is not noticeable at a 50% factor (nicely, the le size has, however, been reduced to 15% of the original). At a 75% factor and above, the loss of pixel information becomes apparent, to the point of yielding an image of poor quality, with observed blocky and blurry artifacts.

Clearly, the amount of compression is a matter of subjectively determining the trade-off between le size and image quality, which depends on the nal application, for instance either a screen wallpaper, a web banner, or a le icon.. See, for instance: http://en datamatrix 2d barcode for .NET .wikipedia.


cfm.. Overview of data compression standards 4:4:4. 4:2:2. 4:2:0. Figure G4 Compressing the tw o 8 8 chroma blocks of 4:4:4, resulting in 1/ size (4:2:2) and 1/ 3 2 size (4:2:0) reductions.. It is beyond the scope of th 2d Data Matrix barcode for .NET is appendix to run into the complex details of image analysis and the JPEG encoding algorithm. Here, I shall just provide a brief summary of the main concepts and features of JPEG.

Digital images are made of two-dimensional (2D) pixel arrays, sometimes referred to as bitmaps. Each pixel is de ned through 3 8 = 24-bit codewords, with eight bits de ning the intensity of each of the red, blue, and green (RBG) components, on a 0 255 scale. This code makes up to 256 256 256 = 16 777.

216 or about 16.7 million possible colors! This original pixel is then analyzed and decomposed into a new color space, which considers three components: one for luminance (or brightness, or luma ), and two for chrominance (or chroma ). Chrominance is another way of labeling colors according to their hue (position in a linear color scale) and saturation (intensity).

The reason for such a conversion is that the luminance and chrominance data offer more possibilities for compression, as we shall see. The JPEG algorithm rst transforms the 2D-RBG pixel array into three 2D arrays, one for luminance and two for chrominance. Each of these arrays is then decomposed into blocks of 64 pixels, which equivalently form 8 8 pixel arrays (each pixel being eight bits).

As the human eye sees more details in luminance, it is possible to throw out some information in the chrominance blocks. This is referred to as downsampling or chroma subsampling.30 There exist many different possibilities of achieving downsampling, of which I will only mention two.

Let us name 4:4:4 the original three-block set, as shown in Fig. G4. As seen from the gure, halving the horizontal data in the last two 8 8 (chroma) blacks results in a set called 4:2:2, which is one-third smaller.

31 Halving the chroma data in both horizontal and vertical directions results in the set 4:2:0, which is one-half smaller. This 4:2:0 compression is the scheme used in most JPEG images, and also in digital video (DV) and high-de nition DV (HDV), in MPEG (including MPEG-2 for the digital video disk or DVD). However, the JPEG compression algorithm does not stop there! The 8 8 blocks (or their reduced versions) are then submitted to discrete cosine transform (DCT), which is analogous to a 2D discrete Fourier transform.

32 The DCT maps each block into its frequency version containing the Fourier coef cients. The coef cients are then subjected to a quantizer, which uses variable step or quantum sizes (smaller for low frequencies, and the reverse). Each coef cient is scanned through a zigzag pattern, then divided by.

30 31 32. See for instance: http://en. .net framework Data Matrix barcode wikipedia.

org/wiki/YUV_4:2:2. The gure does not have the purpose of explaining how the halving in both horizontal and vertical directions is actually performed. See: www.; http://en. transform; http://rnvs.informatik. jan/MPEG/HTML/mpeg_tech.html.

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