Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)

Abstract

Designing an effective and high performance network requires an accurate characterization and modeling of network traffic. The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test. The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three different movies.

SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture

“…Finally, to simulate video feed processing, we define the size of each image frame using the distribution presented in [32] that found that the Pearson distributions are the best fit for the dataset used using both the graphical method and the hypothesis test.…”

Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

“…where F denotes the Fourier decomposition. Comparing (24) with (23), it indicates that the number is equivalent to the number of nonzero harmonic components in the Fourier series of , ( , ), in other words, is equal to the sum of nonzero number in ( ). After the determination of the -value, it is easy to achieve the weighting coefficient ( ), which is in fact the solution of the following equation by solving the Fourier transformation under the polar coordinates:…”

“…Since the visual appearance of a region in the imaged TP is determined by the random layouts of LHFs, the distinctive TP characteristics are inevitably related to the statistical methods [23]. The early widespread methods are the local or global statistics of the images pixel intensities or the image filter responses, such as the first-order and second-order statistics, and cooccurrence matrixes-based statistics, e.g., gray level cooccurrence or difference histograms [24], gray level cooccurrence matrix (GLCM), gray level run length matrix (GLRM) [25], and multivariate image analysis (MIA) [26], as well as their variants [27].…”

“…Some researchers attempt to discover the relation between the visual appearance and the LSD of the texture image's SS in biovisual perception experiments or computational textural modeling methods [23,29]. Considerable efforts have devoted to the statistical distribution modeling-(SDM-) based methods to the LSD characterization to learn a fuller representation of the distribution models based on the random field constraint, especially integrated with the prevalent multichannel and multiscale image analysis, such as dyadic Wavelet transform (DWT) and Gabor Wavelet transform (GWT) [30].…”