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Second, when streaming video data over an unreliable network, some packets may be lost in transit. If the lost packets cannot be recovered, then the affected video frame will suffer from quality degradation. Worse still, if the packet losses affect anchor frames (I and P frames), then the predicted frames will be affected as well. For data loss in a P frame, the predicted B frames and the subsequent P frames will all be affected. Data loss in an I frame is worse as practically all subsequent predicted frames up to the next I frame are affected. Thus, to limit this error propagation problem, a video encoder will periodically introduce I frames to break the temporal dependencies. As an example, the frame sequence in Figure 2.6 reintroduces an I frame, for every 11 predicted frames. Thus, in the worst case of losing some data in the I frame, only up to 12 frames (including the I frame) are affected. With a video frame rate of 25 fps, this translates into slightly less than half a second of quality degradation. An I frame together with the predicted frames is called a group of pictures (GOP). Depending on the encoder, the GOP structure, such as the number of B frames between anchor frames and the number of P frames, is usually con gurable by the user during encoding. Moreover, the GOP structure is not necessarily xed across the whole video stream. More advanced encoders will attempt to align the GOP boundary with scene changes in the video content (e.g., when switching camera) to improve visual quality and/or to further reduce the encoded video bit-rate.

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Expanding a site with necessary functionality is a natural and obvious step in the development of a site. When it comes to WordPress, expanding a site often means that you want to show off or promote content in some way, or add possibilities for interactions, like the login form, for example. The next chapter is about using images to spice up a site; in other words, another type of element that can expand a site s impact. Remember, though: don t go too crazy with these extras. After all, you don t want clutter.

When the network is trained, the normalized softmax output activations v[i] estimate the a posteriori probabilities of the 26 alphabet-letter patterns (Section 6-10c)

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Another side effect of media compression is bit-rate variations. As illustrated in the previous section, different types of video frames (I, P, B) generally consume different amount of bits after encoding. Thus, if we compute the average video bit-rate on a frame-by-frame basis, then the video bit-rate will vary quite substantially across different frames. Figure 2.7 plots the frame size versus frame number for a video encoded in MPEG1. Not surprisingly, the I frames are generally larger than the P and B frames, evident in Figure 2.7. For a media server this bit-rate variation creates a problem. Speci cally, if the media server is to transmit the video data in the exact bit-rate on a frame-by-frame basis,

Frame Size (bytes)

chapter is about displaying images in a WordPress site, beyond the traditional inclusion of illustrative points or inspiring scenery in your posts and pages. It is more about galleries, presentation, and photo-sharing services, not to mention the small matter of actually setting up WordPress image management in a way that makes sense.

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SOFTMAX PATTERN CLASSIFIER --estimates a posteriori probabilities - and implements associative memory -----------------------------------------------------------------nx = 25 | N = 26 ARRAY input[nx], x[nx] ARRAY INPUT[N, nx], TARGET[N, N], W[N, nx] ARRAY v[N], q[N], y[nx], error[N], Yerror[nx] -for i = 1 to N | for k = 1 to nx | -initialize W W[i,k] = ran() | next | next ----------------------------input-pattern rows -A data 1,1,1,1,1 data 1,-1,-1,-1,1 data 1,1,1,1,1 data 1,-1,-1,-1,1 data 1,-1,-1,-1,1 -B data 1,1,1,1,1 data 1,-1,-1,-1,1 data 1,1,1,1,-1 data 1,-1,-1,-1,1 data 1,1,1,1,1 etc for C, D, read INPUT MATRIX TARGET = 1 | -- binary-classifier rows lrate = 005 | Tnoise = 05 | Rnoise = 09 | c = 01 NN=20000 -------------------------------------------------------drun write 'type go for successive recall runs' | STOP ---------display F | -- clear the display t=1 | NN = 2 | restore | -- reset the read pointer label recall for I = 1 to N read input | drun RECALL write v | -- show all 26 probabilities write 'type go for successive recall runs' | STOP next restore | go to recall FIGURE 6-2a This experiment-protocol script for the pattern classifier trains the network with NN = 20,000 noise-perturbed patterns and then calls a second DYNAMIC program segment for successive tests with each pattern Each test estimates the a posteriori probabilities and displays the actual patterns input, x, yy, and y (Fig 6-2b) Try this program with different noise levels Tnoise, Rnoise

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