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As JPEG has been adapted to other environments, additional JPEG schemes have come into practice. JPEG is designed for still images and offers reduction ratios of 10:1 to 50:1. The algorithm is symmetrical, meaning that the time required for encoding and decoding is essentially the same. There is no need for motion compensation and there are no provisions for audio in the basic standard. The JPEG specification, like MPEG-1 and MPEG-2, is often described as a tool kit of compression techniques. Before looking at specifics, it will be useful to examine some of the basics. Remember that many of these underpin current streaming media compression techniques.



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As discussed briefly in previous sections, a compression system reduces the volume of data by exploiting spatial and temporal redundancies and by eliminating the data that cannot be displayed suitably by the associated display or imaging device. The main objective of compression is to retain as little data as possible, but just sufficient to reproduce the original images without causing unacceptable distortion of images [1]. To paraphrase Albert Einstein, the aim is to make things as simple as possible, but no simpler. A compression system consists of the following components:

this approach to calibrate your heliodon. You might like to make some marks on the cardboard surface to indicate different times of day. The next stage of experimentation with the heliodon is to look at modeling a real building.





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Digitization, sampling, and segmentation Steps that convert analog signals on a specified grid of picture elements into digital representations and then divide the video input first into frames, then into blocks. Redundancy reduction The decorrelation of data into fewer useful data bits using certain invertible transformation techniques. Entropy reduction The representation of digital data using fewer bits by dropping less significant information. This component causes distortion; it is the main contributor to lossy compression. Entropy coding The assignment of code words (bit strings) of shorter length to more likely image symbols. This minimizes the average number of bits needed to code an image.

Key terms important to understanding video compression include the following:

Figure 8-13

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The two table adjustments are illustrated in Figure 3-7. In order to secure the table at an angle, probably the easiest way is to use a length of dowel rod with a couple of big lumps of modeling clay at each end. Set the angle of the table to the horizontal, then use the dowel as a prop with the plasticine to secure and prevent movement. There are a couple of simple experiments that we can do with our heliodon to get you started. Remember the sundial that you made earlier in the book Well, set the angle of latitude on your table to the angle that you constructed your sundial for (Figure 3-8). You will see that as you rotate the table, the time on the sundial changes. You can use

Motion compensation The coding of video segments with consideration to their displacements in successive frames (in other words, coding segments of the picture according to how they move over a series of frames). Spatial correlation The correlation of elements within a still image or a video frame for the purpose of bit rate reduction (in other words, looking for segments of the picture that are the same or similar, so that you don t need to fully describe each and every similar segment, you can just instruct the decoder to duplicate a single fully described segment). Spectral correlation The correlation of different color components of image elements for the purpose of bit rate reduction (you guessed it looking for segments of the picture than have the same or similar

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