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the best fit. You don t need much information to send this, compared to the original pixel field s digital representation.* In a decoder, the inverse transform is performed to recreate the pixel block, given information about the colors that must be present. A variety of transforms can be used, but popular ones include the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT). Both of these have the property of describing an impression of a block of pixels using a smaller number of spectral values, not because they are inherently lossy (in fact, they are lossless), but because you can ignore some of the data and get away with it. On reconstruction, what you get is a pixel field not too far from the original pixels analyzed, but effectively synthesized by the inverse transform mathematics. Pixel fields need not be square, but for convenience of computation, they often are. Unfortunately, the result is that you get block effects, since the analysis performed on one region of the image bears no relation to the analysis performed on the next. If you divide the image into a mosaic of square regions and perform discrete cosine transforms on each region independently, when you reconstruct the image from the transform data, there will sometimes be noticeable and potentially sharp differences in color at the edges of the little mosaics. Ways around this problem include using non-square or overlapping regions to create some sort of average. Of course, to get bigger reductions in data, at the expense of greater error when decoding the image, you can choose bigger regions of pixels to transform. Another solution is to use the discrete wavelet transform to avoid blocking artifacts. The wavelet filter is applied to a much wider area of pixels, so doesn t suffer from blocking artifacts, even at relatively large compression ratios. Because wavelet transforms remove high-frequency components of the image, step-by-step, wavelets are excellent at representing edges in the image. Under high compression ratios, the artifacts are localized around image edges, not spread across the whole area of the block, as they are with the discrete cosine transform method. Yet another way to reduce data is to find only those things in the image that have changed since the last frame and encode only those. Using motion analysis, if the background, say, hasn t changed from frame to frame in a given video sequence, you need only instruct the decoder to hold on to those pixels and just change the moving ones.
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*For a more detailed technical explanation of how actual compression algorithms work, refer to Appendix E. The purpose of the current discussion is to give simple analogies describing the effective action of the compression process.
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The above is a gross oversimplification of compression techniques and serves only to refer the interested reader to more in-depth sources. I have used terminology that explains what is going on in a way I can understand it, so my explanations are more impressionistic than rigorously mathematical. What I would like the reader to take away from this discussion is that there are many techniques used to throw data away or to use information that the decoder doesn t need to receive (or receive often) to reconstruct an image. In fact, many proprietary compression schemes don t want you to know how they achieve their particular compromise between good image quality and heavy data reduction, as this is a trade secret and the special sauce in their particular compression products. To me, it is somewhat pointless to argue that one compression scheme gives better quality than another, for a given bit rate. All lossy compression is a compromise. Every technique so far developed will do a great job with some kinds of images, yet reveal the nasty inner workings of the algorithm s throwing away the details, given other images. The art of the compromise is in hiding the fact that some of the detail is missing. This entire catalog of techniques is ultimately used to render an image from a compressed representation of it, so that the human nervous system thinks the image is all right. There is no doubt that many new and ingenious ways of unnoticeably discarding data will be discovered yet. At the time of writing, these compression and encoding technologies were battling for supremacy:
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Microsoft Windows Media 8 RealNetworks Real Video 8 and Real One Sorenson Broadcast 3.1 in conjunction with Apple QuickTime 5 MPEG 4 (various vendors) H.26L
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Some of these use wavelets, some use discrete cosine transforms. This list is by no means exhaustive. For audio, there is an equally impressive array of compression techniques and vendor solutions. Ultimately, the consumers care only that the media plays well and that the player is compatible with the media they want to watch. Several vendors have sought to create multi-codec players (that can decode anybody s compression for example, Generic Media) and other vendors have created machines that encode into everybody s compression formats. To date, the competition between compression schemes has served mainly to create a desire, in the end-user, for a single standard. The
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