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1 Zero MV ratio Nz Percentage of zero MVs in a frame It is the proportion of the frame that does not change at all (or changes very slightly) between two consecutive frames It usually corresponds to the background if the camera is static within one shot 2 Mean MV size M Proportion of mean size of the MVs within one frame normalized to the screen width, expressed in percentage This parameter determines the amount of the global motion 3 Mean non-zero MV size n Proportion of mean size of the non-zero MVs within one frame normalized to the screen width, expressed in percentage This parameter determines the amount of the global motion 4 Intra frame MV standard deviation F Standard deviation of MV sizes in the frame MV standard deviation is expressed as a percentage of the MV mean size in the frame.

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There are three different functions to convert a variable to Integer: CInt, Int and Fix. Although in most documentations, CInt is used, this is dangerous. All three functions behave differently when converting negative numbers. Only Fix limits itself to stripping the decimal places of a number. CInt and Int both automatically round negative numbers to the next lower or next higher integer. This can cause a lot of confusion and errors. Therefore, always use Fix to convert numbers to Integer. In addition, you can use functions to format a variable:

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This feature is sensitive at dynamic motion changes 5 Intra frame standard deviation of non-static blocks f Standard deviation of the sizes of non-zero MVs, expressed as a percentage of the mean size of these MVs in the frame It is a measure of the uniformity or dynamic changes of the moving regions 6 Uniformity of movement d Percentage of MVs pointing in the dominant direction (the most frequent direction of MVs) in the frame For this purpose, the granularity of the direction is 10 degrees 7 MV direction uniformity of non-static blocks B Percentage of MVs pointing in the main direction in the moving regions (zero MVs are excluded) A Principal Component Analysis (PCA) was carried out on the obtained dataset PCA is a suitable method (Krzanowski 1988) for viewing a high-dimensional set of data in considerably fewer dimensions (see Figure 1111).

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The rst two components proved to be suf cient for an adequate modelling of the variance of the data Finally, the biplot technique (Gabriel 1971) was applied for representing the rst two principal components by means of parameter vectors The biplot provides a useful tool of data analysis and allows the visual appraisal of the structure of large data matrices It is especially revealing in principal component analysis, where the biplot can show inter-unit distances and indicate clustering of units as well as display variances and correlations of the variables The visualization of PCA results shows that all parameter vectors have approximately similar in uence on the dataset Therefore, it was not possible to set hard decision criteria for selection of video quality parameters according to PCA results The chosen parameters have low computational complexity and good distribution over the most signi cant subspace of PCA within all content classes.

By the total probability property,

According to PCA results and computational complexity, the following three parameters were chosen:.

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Figure 11-7

the key mechanism for recovering the message as we can now extract the message from the chaotic carrier by simply subtracting the chaotic signal reproduced in the receiver laser from the transmitted signal. Looking at the spectrum in Figure 9.11(a), it might be suspected that the message may be extracted by filtering the waveform with a narrow bandpass filter at the message frequency. Figure 9.12 shows the filtered waveforms for (a) the extracted message signal, (b) the transmitted signal, and (c) the receiver output, all obtained using a narrow bandpass

1

When you want to show that an action is in a simple tense (present simple, past simple, or future simple) you put an individual point on the timeline with an X or a spot and label it, as in Figure 5-1.

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