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Figure 12.6 shows two segments of the trace of lm, and it can be seen that there are several spikes that are possibly due to scene changes. Since these spikes may be a dominant cause of cell losses, we need to model both their spacing and the magnitude. If we use merely xed spacing at the correct rate but do not model the distribution of their spacing, multiplexed sources with nonidentical starting points will not have coincident spikes from time to time. This will underestimate cell losses. Since we do not have a video record of the sequences, we will assume that a scene change occurs when a frame contains an abnormally large number of cells compared to its neighbors. We make this notion quantitative in the following way. Let Xi be the number of cells in frame i. At a scene change, the second difference Xi 1 Xi Xi Xi 1 will be large in magnitude and negative in sign. To quantify what we mean by large, we divide the second difference by the average of the past few frames. We found that using 25 frames (1 second) in the average was about the same as using 6 frames (about 1 second), and the latter was adopted. We 4 chose 0:5 as the critical value; this choice is entirely subjective. It would be nice if the subjectivity could be replaced by an objective criterion. We examined the statistical theory of outlier identi cation for guidance and concluded that this is wishful thinking because objective tests need to have ``outlier'' speci ed externally. To see if our criterion identi ed scene changes accurately, we looked at the time series Xi and the corresponding values of the scaled second differences. Some of the



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data are shown in Fig. 12.6. The rst 1000 frames are shown on the left. The test values identify the scene changes with no false positives. The choice of 0:5 as the critical value is not signi cant. Frames 9001 through 10,000 are shown on the right. This is a more active (in terms of bit-rate uctuations) subsequence, and changing the critical value will affect the number of frames identi ed as scene changes. There are 317 scene changes when 0:5 is the critical value (the mean scene length is 6.5 seconds); there are 374 when 0:4 is used, and 283 when 0:6 is used. The density functions of the scene lengths produced by these critical values are shown in Fig. 12.7. We observe that the critical value does not have a large effect on the density function. 12.3.2.1 Scene Lengths Plots of the autocorrelation function showed that scene lengths are uncorrelated, so the main modeling issue is to characterize the distribution of the number of frames in a scene. The shape of the density in Fig. 12.7 was observed on all sequences except for news. (This is a news broadcast. There are 134 scene changes, and 75 of them are 3 frames long. Moreover, most of these occur consecutively. We do not have an explanation for this. The 3-frame scenes were deleted and the remainder are called news.d.) This unimodal and long-tailed shape is characteristic of distributions used in reliability and insurance-loss models. We will use the following three distributions as candidates for describing scene lengths (and the number of cells per frame in the sequel).





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The Gamma Distribution. The density function is given by Eq. (12.3). The Weibull Distribution. It has the complementary distribution function (the probability that the random variable is larger than x) SW x 1 e lx ;

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