The associative multifractal process: a new model for computer network traffic flows

Millán, Ginno; Osorio-Comparán, Román; Lomas-Barrié, Víctor; Lefranc, Gastón

doi:10.1109/chilecon54041.2021.9703007

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…The application of fractal properties to the network traffic characterization offers simple models but also limits their accuracy. As the traffic with variable bit rate leads to the creation of a structure with changes on different scales, a more detailed characterization of the traffic is possible using multifractals [20,21]. In this research, the characterization of the 3D video format was also performed using multifractal parameters.…”

Section: Introductionmentioning

confidence: 99%

Fractal and Multifractal Characterization of 3D Video Signals

Zekovic

2023

Fractal Analysis - Applications and Updates

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Video signals are responsible for the largest amount of information in data storage and data transmission systems. Three-dimensional (3D) video formats are the largest in terms of the amount of data, and thus the required bit rate. For the efficient transmission of 3D video in communication systems, a detailed knowledge of the traffic characteristics of the format is necessary. In this research, characterization of 3D video signals is performed by using fractal and multifractal analyses. Codes for analyses are written in MATLAB and Python. Communication network traffic shows self-similar behavior with long-range dependence (LRD). Using visual methods and a rigorous statistical methods, it was shown that video sequences of 3D video formats have fractal self-similar properties with LRD and high Hurst parameter values. It is shown that investigated 3D video formats have multifractal structure by using the histogram method. The research included different 3D video formats, specifically with one video sequence (frame compatible, FC and frame sequential, FS) and with two or more video sequences (multiview, MV). Cases with different signal qualities defined by the quantization parameter, different types of frames, different groups of picture (GoP), and different broadcasting methods were taken into account and separately analyzed.

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Section: Introductionmentioning

confidence: 99%

Fractal and Multifractal Characterization of 3D Video Signals

Zekovic

2023

Fractal Analysis - Applications and Updates

View full text Add to dashboard Cite

show abstract

The associative multifractal process: a new model for computer network traffic flows

Cited by 1 publication

References 15 publications

Fractal and Multifractal Characterization of 3D Video Signals

Fractal and Multifractal Characterization of 3D Video Signals

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