H.264/AVC Video Compressed Traces: Multifractal and Fractal Analysis

Abstract

Publicly available long video traces encoded according to H.264/AVC were analyzed from the fractal and multifractal points of view. It was shown that such video traces, as compressed videos (H.261, H.263, and MPEG-4 Version 2) exhibit inherent longrange dependency, that is, fractal, property. Moreover they have high bit rate variability, particularly at higher compression ratios. Such signals may be better characterized by multifractal (MF) analysis, since this approach describes both local and global features of the process. From multifractal spectra of the frame size video traces it was shown that higher compression ratio produces broader and less regular MF spectra, indicating to higher MF nature and the existence of additive components in video traces. Considering individual frames (I, P, and B) and their MF spectra one can approve additive nature of compressed video and the particular influence of these frames to a whole MF spectrum. Since compressed video occupies a main part of transmission bandwidth, results obtained from MF analysis of compressed video may contribute to more accurate modeling of modern teletraffic. Moreover, by appropriate choice of the method for estimating MF quantities, an inverse MF analysis is possible, that means, from a once derived MF spectrum of observed signal it is possible to recognize and extract parts of the signal which are characterized by particular values of multifractal parameters. Intensive simulations and results obtained confirm the applicability and efficiency of MF analysis of compressed video.

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

“…Different complex structures, signals and phenomena have been successfully analyzed and described through the MF approach: for instance, image processing [29][30][31][32][33][34][35][36][37], medical signal processing [28,35,38,39], analysis of the road traffic [40] and modern multimedia traffic [41,42], even the analysis of climate changes [43][44][45]. The MF analysis permits us to describe signal/structure features both from local and global points of view.…”

4K video traffic prediction using seasonal autoregressive modeling

“…Most of them are based on the Markov chain, wavelet and fractal techniques, ARIMA, Fractional ARIMA (FARIMA), etc. [15][16][17][18]. In this paper we apply Seasonal ARIMA (SARIMA) model, according to [4] as a model that is simple to implement, without requiring complex steps.…”

Multifractal analysis of 3D video representation formats

“…Alternatively, quantity α is called singularity strength, while f (α) represents singularity or Hausdorff singularity, and f (α) curve is labeled as f (α) singularity spectrum. Singularity α follows local changes in the signal, while f (α) provides global characteristics of data, [28,29,[42][43][44]].…”