Multifractal analysis of 3D video representation formats
- Amela Zeković^{1, 2}Email author and
- Irini Reljin^{1}
https://doi.org/10.1186/1687-1499-2014-181
© Zekovic and Reljin; licensee Springer. 2014
Received: 1 March 2014
Accepted: 21 October 2014
Published: 3 November 2014
Abstract
One of the main properties of a three-dimensional (3D) video is the large amount of data, which impose challenges for network transport of videos, in applications such as digital video broadcast (DVB), streaming over IP networks, or for transmission over mobile broadband. Addressing these challenges requires a thorough understanding of the characteristics and traffic properties of 3D video formats.
We analyzed 3D video formats using publicly available long video frame-size traces of videos in full high definition (HD) resolution with two views. Examined 3D video representation formats are the multiview (MV) video format, the frame sequential (FS) format, and the side-by-side (SBS) format. We performed a multifractal analysis through extensive simulation and showed multifractal properties of 3D video representation formats. It was shown that the MV video had the highest multifractal nature, while the FS video had the lowest. Also, a part of the multifractal spectrum connected to the highest changes in the signal (high bitrate variability) is analyzed in detail. Changes in multifractal properties for different streaming approaches of 3D videos with aggregated frames are examined, as well as the influence of frame types and values of quantization parameters. Multifractal analysis was performed by the method of moments and by the histogram method.
Keywords
3D video Multifractal spectrum TransportIntroduction
A three-dimensional (3D) video contains several views of a video scene, which provide depth perception for a viewer. 3D video representation formats with one frame sequence are labeled as the frame compatible format, ones with two frame sequences are the stereoscopic multiview format, while ones with more video sequences as the multiview video format [1–5].
The quantity of data for the multiview video representation format is significantly higher than in the case of the conventional single-view video and presents a restriction on storage and transmission of the video. As the number of applications for this video constantly grows, beyond already steadily present cinema applications, towards home and mobile uses, several important issues should be addressed and some problems resolved.
The move from multiuser applications of 3D video towards single-user applications imposes requests for improvement of coding (dealt with in [6–8]), equipment for production of 3D videos [9], and equipment for displaying [10]. An equally important question is the transmission of 3D video formats.
Previous studies of transport characterization of 3D videos are often dedicated to analysis of protocols for delivering 3D video representation formats [11–13]. Other line of research has quality of video as a central subject, for instance, a video-quality-aware routing algorithm for 3D video transmission in wireless networks is presented in [14], while quality-of-experience aware rate adaptation methods for 3D videos are discussed in [15].
In this paper, we present results of research in 3D videos with two views in the multiview (MV) video format, the frame sequential (FS) format, and in the side-by-side (SBS) representation format. We have used publicly available, long frame-size traces (51,200 frames), in full HD 1,920×1,080 pixel resolution [4, 16]. For characterization of a video and for the network performance evaluation, video traces are often used [17–19]. In our multifractal analysis of 3D video representation formats, we used publicly available long frame-size traces. Analyzed videos had constant values of quantization parameters, indicating a variable bitrate, which is important in the sense of quality, small delay, and higher multiplexing gain [20, 21], and also allow to provide multifractal characteristics of the signal from basic standardized encoders that were selected for 3D videos (JSVM 9.19.10 was used for the FS and the SBS formats [22], while JMVC 8.3.1 for the MV 3D video was chosen [23]).
We used the method of moments and the histogram method to calculate multifractal properties of 3D videos. With multifractal characterization by multifractal spectrum and by generalized dimensions, we found that among the views of the multiview video, the highest burstiness is for the combined view (CV), followed by the left view (LV), being the lowest for the right view (RV). Among different representation formats of 3D videos, MV, FS, and SBS, the MV video has the highest burstiness, followed by the SBS format, while the best results are achieved for the FS format.
Streaming with a merging approach is applied for MV and FS representation formats for aggregation of two consecutive frames and for all frames in one group of pictures (GoP). This streaming approach shows significant improvement in variability characteristics, showed by multifractal spectrum, in the case of the MV video over FS video. The bitrate variability shown in [4], by the means of a coefficient of variation (CoV) and a variability distortion (VD) curve, yields to a very similar conclusion.
Our results, obtained by multifractal analysis, can be helpful for the development and improvement of multifractal network traffic models, [24–27], regardless of the investigated 3D video formats. The exact multifractal model can be derived by the investigation of multifractal spectra of known and easily generated multifractal signals such as binomial and multinomial cascades [28, 29], in comparison to the multifractal spectra of different 3D video formats provided in this paper, using two different methods, the method of moments and the histogram method. Also, for appropriate model realization, the values of generalized dimensions provided in the paper can be beneficial.
Management and control of video traffic in current and future applications in a variety of networks [30–32] can be improved having in mind detailed characterization of 3D video representation formats, provided in this paper. Also, given the variability of the examined 3D videos, for real-life applications, some bandwidth management techniques are necessary, such as traffic smoothing [20, 33, 34] and statistical multiplexing [35–37]. These are the areas where our results can also be beneficial.
3D video representation formats
In this section, an overview of the 3D video representation formats, their coding principles, and streaming approaches are presented, [4, 5, 38, 39]. We analyze and compare three main 3D video representation formats: the MV video, the FS video, and the frame compatible (FC) video formats.
Overview of 3D video formats
The MV video contains several views, where each view v, v = 1,…,V is one frame sequence. This video format has full resolution of the underlying spatial format in each view. Also, frame rate f for each view v of the MV video is the same as in the underlying temporal format. For instance, for full HD 1,920 × 1,080 pixel MV video format with a frame rate f = 24 frames/s, each view has 1,920 × 1,080 pixel frames and frame rate f = 24 frames/s. For the coding of the multiview video format, multiview video coding (MVC) is used. This type of coding, in addition to temporal and spatial redundancy, utilizes inter-view redundancy. Thus, ITU reference software, referred to as JMVC, first encodes frames of the left view and then uses these frames as reference frames for encoding frames of the right view [23].
The FS video format has only one frame sequence, where frames from different views are interleaved. The spatial resolution of the FS format is the same as in the underlying spatial format. The frame rate of FS format is Vf, where V is number of frames and f is the frame rate of the underlying temporal format. Coding of the FS video format is done by a conventional single-view video encoder, such as JSVM reference implementation of the scalable video coding (SVC) extension of advanced video coding (AVC) encoder [22].
Frame compatible (FC) formats allow utilization of existing infrastructure and equipment for transmission and services for 3D videos. This format has one video sequence with frame rate f that is the same as in the underlying temporal format. FC formats have lower spatial resolution than the underlying spatial format. For example, for the most widely used FC format, the SBS format, frames are spatially sub-sampled in horizontal direction. For instance, for full HD 1,920 × 1,080 resolution, the left and the right views of the SBS format have 960 × 1,080 pixel frames. These sub-sampled frames are interleaved into one frame in full HD resolution. As in the case of the FS format, SBS representation also uses conventional single-view video encoder for coding.
3D video traces
For an evaluation of multifractal properties in order to estimate traffic characteristics of 3D videos, long, publicly available, frame-size video traces are used [4, 16]. We examined 3D videos with two views (V = 2) in the MV, the FS, and the SBS representation formats. Coding of the multiview video is performed by applying reference software JMVC (version 8.3.1), while for coding of FS and SBS formats, H.264 reference software JSVM (version 9.19.10) in a single-layer encoding mode is used. Each view had 51,200 full HD 1,920 × 1,080 pixel frames and the frame rate f = 24 frames/s. We performed evaluations with Tim Burton’s movie Alice in Wonderland, which is a movie with a combination of live action and computer animation. We analyzed different 3D representation formats of this movie and videos with different quantization parameter settings. The values of quantization parameters in the main analysis were q_{ p }(I,P,B) = (28,28,28). Additionally, videos with quantization parameters q_{ p }(I,P,B) = (24,24,24), q_{ p }(I,P,B) = (34,34,34), where the parameters that are the same among the frame types, and videos with quantization parameters q_{ p }(I,P,B) = (15,15,21), q_{ p }(I,P,B) = (20,20,26), q_{ p }(I,P,B) = (24,24,30), and q_{ p }(I,P,B) = (30,30,36), have been analyzed as well. In the main analysis, GoP length for the MV and SBS formats was 16 frames. The FS format had GoP with 32 frames, which means that all encodings have the same playback time between intracoded (I) frames. GoP pattern in the main analysis was B1, which means one bi-directional (B) frame between successive intracoded (I) and predictive encoded (P) frames, while additional consideration was performed on B7 pattern videos.
Streaming of 3D videos
Streaming of the SBS representation format is performed frame by frame, where each frame is integrated from spatially sub-sampled frames from the LV and the RV, and with the same frame rate as with the underlying temporal format. Streaming of the MV representation format can be performed in several different ways. The basic way of streaming the MV video is to stream each view individually. A second streaming option is to perform some kind of merging of views, such as sequential (S) merging or aggregation by combining (C). With sequential merging, frames from different views are used to form one sequence in the following order: first view 1 of frame 1, followed by view 2 of frame 1,…, followed by view V of frame 1, followed by view 1 of frame 2 … We also name this signal as the CV. With aggregation streaming approach, multiview frames are formed, where one multiview frame is the sum of all frames with the same frame number from different views. For the FS format, a sequential and aggregation streaming approach can be applied. Aggregation of frames on the level of two frames performs smoothing of the data across V = 2 views. This approach can be further extended on the level of 16 frames, one GoP of the encoder. Aggregations of two frames are labeled as CV-C 2 for the multiview representation format and FS-C 2 for the frame sequential format, while aggregations of 16 frames are labeled as CV-C 16 and FS-C 16 in the following text.
Estimation of multifractal properties
Fractals can be viewed as sets with visual expression of a region drawn in black ink against white paper. Most natural phenomena cannot be expressed in terms of contrast between black and white, and they demand more general mathematical objects that embody the idea of ‘shades of gray’. These more general descriptors are called measures. For instance, measures can represent level of ground water, pixel values from pictures, or frame sizes as in our case. When a measure performs high variability at all scales, and when the variability is the same at all scales, or at least statistically the same, one says that the measure is self-similar or that is multifractal. Self-similar sets have a property that each piece (regardless how small) is identical to the whole after some rescaling and translation, [28, 29, 40, 41].
A process that fragments a set into smaller and smaller components according to a rule and at the same time fragments the measure of the components by another rule is called a multiplicative process or cascade. The simplest multiplicative process is a binomial cascade.
For characterization of multifractals, only one number, such as fractal box-counting dimension D, is not sufficient. If a set S supporting a measure μ is covered by boxes of size ε and if the number of boxes N(ε) is evaluated, one can determine box-counting dimension as N(ε) ∼ ε^{-D}. A problem with this characterization is that the value of the measure in each box is disregarded.
called coarse Hölder exponent. This quantity is the logarithm of measure of the box over the logarithm of the size of the box. Usually, α is restricted to a region [α_{min},α_{max}], where 0 < α_{min} < α_{max} < ∞.
are required. This function for ε → 0 converges to a limit f(α). The definition of f(α) means that for decreasing the box size ε, the number of boxes with coarse Hölder exponent equal α, N_{ ε }(α), increases by the scaling relation N_{ ε }(α) ∼ ε^{-f(α)}. Function f(α) describes distribution of α. Graph f(α), usually called a multifractal spectrum or f(α) curve, has for some simple types of multifractals (such as binomial cascade) shape of mathematical symbol ⋂. For some multifractals f(α), the curve can lean to one side.
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, 42–44].
An empirical self-similar measure has only one, n th, stage of measure known. So, for the evaluation of multifractal spectrum, previous stages of measure reconstructed by coarse-graining the measure are necessary. Given the discrete data, the smallest measures are the given n th stage, and it is for the size of the boxes ε = 1. Sum of all measures in a stage is one, or normalized to one.
In our research, two methods for obtaining an estimate of f(α) curve are used: the method of moments and the histogram method. The method of moments is chosen as a method in which f_{ ε }(α) converges to f(α) the fastest, resulting in a short execution time. The second histogram method has slower convergence of f_{ ε }(α) to f(α) and slower execution, but has a tendency to show additive processes in signal and allows inverse multifractal analysis (determination of the exact part of data with chosen values of pair (α,f(α))). Also, these two methods are different in the way they handle the data, where the method of moments tends to smoothen the data, while the histogram method handles raw data and has less approximation.
The method of moments
which is known as Legendre transform is performed [28]. Plotting f(α) versus α gives an estimation of the multifractal spectrum.
Values D_{ q } are known as generalized dimensions [29, 45]. Especially interesting are dimensions for q = 0, q = 1, and q = 2; D_{0}, D_{1}, D_{2}, respectively. Dimension D_{0} is usually called the fractal dimension, dimension D_{1} is the information dimension, while dimension D_{2} is called the correlation dimension. Dimension D_{0} is equal to the maximum of the multifractal spectrum f(α), when the most probable α occurs, labeled as α_{0}. Dimension D_{1} is called information, because it is proportional to μ log(μ) that scales similarly to the information for probability distribution. The correlation dimension D_{2} defines probability that two randomly chosen points are on the distance grater than ε. These generalized dimensions and D_{ q } spectra are evaluated for our data using (5).
It has been shown that minimal values of multifractal spectra correspond to q → - ∞ for α_{max} and to q → ∞ for α_{min}. Also, maximal values of partition function are found for (α_{min},f(α_{min})), while minimal values occur for (α_{max},f(α_{max})) [29].
An algorithm for evaluation of multifractal spectrum by the method of moments is also implemented using sliding boxes, instead of non-overlapping, for covering the measure. The results for these additional tests are the same as in the previous method for q > 0, but for q < 0, the multifractal spectrum for sliding boxes shows missing undefined values of the spectrum. This is characteristic of some fractals as reported in [28].
The histogram method
The histogram method of determining multifractal spectrum starts with covering the measure with boxes of size ε. In the case of this method, f_{ ε }(α) slowly tends to f(α). So, for better estimation, instead of non-overlapping boxes, sliding boxes are used to cover the measure.
We used n_{ ε } = 8 different sizes of boxes, with the following values ε = [1,3,5,9,13,21,29,37], and therefore ε_{ k } is indexed with k = 1,2,…,n_{ ε }. For each ε_{ k }, total measure of the boxes is determined, μ_{i,k}, where i = 1,2,…,n. Length of the data and ε_{ k } values determine the value of n, where for the smaller box size, the larger number of total measures exists. For easier calculation, all measures are stored in matrix M, where the size of the matrix is [n_{ ε } × n], for the largest possible n (the smallest ε_{ k }). Coarse Hölder exponents α_{ i }, i = 1,2,…,n are now determined as slopes of plots log(μ_{ i },k) versus log(ε_{ k }/L), where L is the length of one-dimensional data.
A range of α values, [α_{min},α_{max}] is discretized in D = 100 pieces of equal length Δα and values α_{ d } are formed as centers of the intervals. In the domain of α values α_{ i }, for different values of j,j = 1,3,5,…,199 number N_{ j }(α_{ d }) is determined, as a number of boxes of size j that have α_{ i } value in the region of Δα around α_{ d }. This procedure is conducted for all values of α_{ d },d = 1,2,…,100. Finally, f(α_{ d }) values are calculated as slopes of plots - log(j/n) versus log(N_{ j }(α_{ d })). Graph f(α_{ d }) versus α_{ d } is an estimation of f(α) curve.
For discussion of multifractal properties, it is important to know that α_{min} corresponds to the highest value of the measure, while α_{max} is related to the smallest and the smoothest data.
Simulation results
We examined 3D video representation formats: the MV video and its different views (the LV, the RV, and the CV), the FS format, and SBS format, discussed in the previous section. Multifractal properties of 3D video representation formats are calculated using the method of moments and the histogram method. First, we present the results obtained by the method of moments, that has a higher level of approximation, and later, the results by the histogram method that handles raw data.
Multifractal analysis by the method of moments
Multifractal spectra
In this section, calculated multifractal spectra of examined 3D video representation formats by the method of moments are presented. We first analyze spectra of the views of the multiview video and spectra of different 3D video formats; then, we proceeded to examine multifractal spectra of different streaming approaches of the videos, influence of quantization parameters values, and frame types on multifractal properties.
Comparison of multifractal properties of 3D video formats obtained by the method of moments
3D video | f _{ max } | α(f_{ max }) | α _{ min } | α _{ max } | D _{0} | D _{1} | D _{2} |
---|---|---|---|---|---|---|---|
LV | 1.0003 | 1.1007 | 0.6574 | 1.2898 | 1.0000 | 0.9013 | 0.8195 |
RV | 1.0001 | 1.0216 | 0.8377 | 1.1577 | 1.0000 | 0.9799 | 0.9585 |
CV | 0.9994 | 1.0717 | 0.6023 | 1.1844 | 1.0000 | 0.8990 | 0.7893 |
SBS | 1.0002 | 1.1003 | 0.6585 | 1.2746 | 1.0000 | 0.9016 | 0.8231 |
FS | 1.0001 | 1.0445 | 0.7119 | 1.2608 | 1.0000 | 0.9456 | 0.8736 |
CV-C 2 | 1.0002 | 1.0641 | 0.7354 | 1.2182 | 1.0000 | 0.9375 | 0.8831 |
CV-C 16 | 1.0007 | 1.0234 | 0.7668 | 1.7303 | 1.0006 | 0.9800 | 0.9593 |
FS-C 2 | 1.0000 | 1.0222 | 0.8508 | 1.0804 | 1.0000 | 0.9735 | 0.9406 |
FS-C 16 | 1.0006 | 1.0144 | 0.7331 | 1.5892 | 1.0005 | 0.9857 | 0.9672 |
Generalized dimensions
In addition to multifractal spectra, the method of moments allows calculation of generalized dimensions not only from a multifractal spectrum (as other methods), but also directly calculating from values of τ and q. We have chosen the latter approach.
In Table 1, generalized dimensions D_{0} (the fractal dimension), D_{1} (the information dimension), and D_{2} (the correlation dimension) for 3D video representation formats are given. Dimensions D_{0} are approximately in all cases equal to 1. Values of f_{max}, given in Table 1, are actually values of D_{0} directly from the multifractal spectrum, and they are all also very close to 1. These values mean that the mostly present fractal dimension is approximately equally probable for all 3D video representation formats. If we order 3D video formats by their values of information and correlation dimensions, as presented in Table 1, a very similar regularity would be observed. The highest correlation dimension is in the case of the CV video, followed by LV, SBS, FS, and RV videos. In the research about traffic characteristics of 3D video formats [4], where the same 3D videos are used, the order of the videos by CoV criteria is exactly the same as in our results for the order of the video by values of the correlation dimension D_{2}. Aggregation on the level of 2 frames of the videos leads to lower values of CoV and closer characteristics of the CV and FS formats (CV CoV moved from 1.3334 to 1.0731 and for FS moved from 1.0338 to 0.8108), which is consistent with higher and closer values of the correlation dimensions for these formats. Values for CoV for aggregation of 16 frames (on the level of GoP) for CV and FS for the movie Alice in Wonderland are not given in [4], but by repeating and extending their research, we found CoV for CV-C 16 to be 0.7416 and for FS-C 16 0.6507, which means that CV and FS videos in the sense of coefficient of variation are even closer together. The same regularity can be observed by looking in correlation dimensions D_{2}, given in Table 1, where the dimensions are getting higher and closer. According to the values of CoV and D_{2}, the FS 3D format has slightly smoother traffic than the CV multiview 3D format, even with aggregation, but based on the burstiness, that is, the highest for the smallest value of α_{min}, CV-C 16 has better properties than FS-C 16, as presented in our results of the multifractal analysis given in Table 1.
Multifractal analysis by the histogram method
Multifractal spectra
3D video representation formats are examined in a multifractal sense using the histogram method. Multifractal spectra are provided having in mind different views of the multiview video, different 3D video formats, different streaming approaches, quantization parameter values, and frame types.
Multifractal spectrum of RV has two dominant bumps in the top of the spectrum, as a consequence of the two processes present in the data - P and B frame types in the signal that are formed using LV frames as a reference. Similar, but a less distinctive process, is present in the case of CV spectrum. Additive processes could not be observed in multifractal spectra by the method of moments, because that method has a higher level of approximation. An advantage of the histogram method for multifractal spectra is the ability to show these processes. The method is used for the reason that it is interesting for examining influences of the system on data, such as the network parameters influence on the data traffic.
Comparison of multifractal spectra properties obtained by the histogram method; ${\mathit{S}}_{\mathbf{1}}\mathbf{=}{\mathbf{\sum}}_{\mathit{d}\mathbf{=}\mathbf{1}}^{{\mathit{n}}_{\mathit{d}}}\mathit{f}\mathbf{(}{\mathit{\alpha}}_{\mathbf{\text{min}}}\mathbf{+}\mathbf{(}\mathit{d}\mathbf{-}\mathbf{1}\mathbf{\left)}\mathbf{\Delta}\mathit{\alpha}\mathbf{\right)}$ , ${\mathit{n}}_{\mathit{d}}\mathbf{=}\frac{\mathbf{1}\mathbf{-}{\mathit{\alpha}}_{\mathbf{\text{min}}}}{\mathbf{\Delta}\mathit{\alpha}}$ ; ${\mathit{S}}_{\mathbf{2}}\mathbf{=}{\mathbf{\sum}}_{\mathit{d}\mathbf{=}\mathbf{1}}^{{\mathit{k}}_{\mathit{d}}}\mathit{f}\mathbf{(}{\mathbf{\alpha}}_{\mathbf{\text{min}}}\mathbf{+}\mathbf{(}\mathit{d}\mathbf{-}\mathbf{1}\mathbf{\left)}\mathbf{\Delta}\mathit{\alpha}\mathbf{\right)}$ , ${\mathit{k}}_{\mathit{d}}\mathbf{=}\frac{{\mathit{\alpha}}_{\mathbf{\text{max}}}\mathbf{-}{\mathit{\alpha}}_{\mathbf{\text{min}}}}{\mathbf{\Delta}\mathit{\alpha}}$
3D video | α _{ min } | α _{ max } | B _{ m } | S _{1} | S _{2} |
---|---|---|---|---|---|
LV | 0.2552 | 2.2338 | 1.9798 | 5.7506 | 16.7234 |
RV | 0.5073 | 2.0830 | 1.5757 | 5.1741 | 13.2493 |
CV | 0.1786 | 2.0400 | 1.8614 | 6.2659 | 17.2827 |
SBS | 0.2532 | 2.0540 | 1.8007 | 6.1907 | 18.1982 |
FS | 0.3536 | 1.7477 | 1.3941 | 6.0029 | 17.0272 |
CV-C 2 | 0.3491 | 2.1756 | 1.8265 | 5.9194 | 15.4685 |
CV-C 16 | 0.6945 | 2.1328 | 1.4382 | 6.5116 | 15.0897 |
FS-C 2 | 0.5372 | 1.8256 | 1.2884 | 5.7611 | 13.3861 |
FS-C 16 | 0.7091 | 2.2251 | 1.5159 | 5.1663 | 13.8045 |
Inverse multifractal analysis
Conclusions
We analyzed properties of 3D video representation formats: the MV video representation format with multiview video coding and the FS and the SBS formats coded with a conventional single-view video encoder. We determined multifractal properties by the method of moments and by the histogram method for three main 3D video representation formats with two views using long publicly available HD 1,920×1,080 resolution video frame-size traces.
We showed that 3D video formats are multifractal and can be modeled as such in traffic network models. In the paper, we present and compare the obtained multifractal spectra as a whole and isolate and compare important points of the spectrum - the most probable singularity of the spectrum that describes multifractal nature of the structure in the most probable case and the smallest value of singularity that is related with the highest burstiness (high bit variability) of the videos. Our results show that MV has the highest bitrate variability and the highest multifractal nature (for the most probable singularity in the spectrum), while the FS video format has the smallest values of these parameters. Obtained results for bitrate variability of 3D videos are compared with the values of a traditionally used statistical parameter for this purpose, the CoV, and showed good agreement.
Our analysis shows and compares multifractal properties of 3D videos with different quantization parameters. It was found that a video with a higher value of quantization parameters (higher compression ratios) shows higher multifractal nature, as well as higher burstiness. Isolated intracoded (I) frames, predictive encoded (P) frames, and bi-directional (B) frames are analyzed. It is shown that I frames have very similar multifractal spectra regardless of the 3D video representation format. It was also shown that B frames, the smallest frames, have the narrowest multifractal spectrum. With the highest bitrate variability (the smallest value α_{min}), P frames show rare prominent parts of the signal, while in comparison, I frames have higher α_{min} but with the higher probability of this singularity.
Results presented in this paper can be beneficial for the traffic smoothing improvement and for the design of more efficient statistical multiplexing. Elementary smoothing techniques over video frames assume their aggregation [20]. The results of the multifractal analysis on the signals with performed smoothing technique based on aggregation of adjacent frames, for all types of the examined 3D video formats, are presented. The smoothing approach is applied for 2 frames (the number of views in the examined 3D video in all formats), as well as for 16 frames (the number of frames in the group of pictures), showing that generally there is lower variability in the signal by using this approach. Particularly significant improvement in the values of multifractal characteristics was noticed for the MV video. These results can further be used for improving the smoothing techniques of 3D videos, in applications such as smoothing with prefetching [33, 46], more precisely in the sense of estimating the bursty traffic, in the process of its management and control, in order to handle it first.
Multifractal parameters calculated in the analysis (multifractal spectra and generalized dimensions) can be used for creating improved multiplexing methods. In [47], fractal properties are used for creating an efficient multiplexing method. Statistical multiplexing methods that pay special attention on the type of frames for their improvement [36] can also potentially improve the performances, having in mind multifractal properties of different frame types that we provided in the paper.
Determined multifractal properties of 3D representation formats have possible application in statistical multiplexing, to develop methods for selection of optimal multiplexer parameters and/or better utilization of available network capacities. Also, the results can be used to analyze how introduction of 3D formats in the same multiplexer with 2D formats affects characteristics of the channel. A complete understanding of the multifractal properties will contribute to the analysis of the behavior of a 3D video signal in a statistical multiplexer, which is a subject of current research.
Declarations
Authors’ Affiliations
References
- Merkle P, Müller K, Wiegand T: 3D video: acquisition, coding, and display. IEEE Trans. Consum. Electron 2010, 56(2):946-950.View ArticleGoogle Scholar
- Chen Y, Wang Y-K, Ugur K, Hannuksela MM, Lainema J, Gabbouj M: The emerging MVC standard for 3D video services. EURASIP J. Adv. Signal Process 2009, 2009(786015):1-13.Google Scholar
- Smolic A, Mueller K, Merkle P, Fehn C, Käuff P, Eisert P, Wiegand T: 3D video and free viewpoint video - technologies, applications and, MPEG standards. In Proceedings of the IEEE International Conference on Multimedia and Expo: 9-12 July 2006. Toronto; 2006:2161-2164.View ArticleGoogle Scholar
- Pulipaka A, Seeling P, Reisslein M, Karam L: Traffic and statistical multiplexing characterization of 3D video representation formats. IEEE Trans. Broadcasting 2013, 59(2):382-389.View ArticleGoogle Scholar
- Fernando A, Worrall S, Ekmekcioglu E: 3DTV: Processing and Transmission of 3D Video Signals. John Wiley & Sons, Inc., UK; 2013.View ArticleGoogle Scholar
- Vetro A, Matusik W, Pfister H, Xin J: Coding approaches for end-to-end 3D TV systems. Proceedings of the Picture Coding Symposium, 15 Dec. 2004 2004.Google Scholar
- Vetro A, Wiegand T, Sullivan GJ: Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 AVC standard. Proc. IEEE 2011, 99(4):626-642.View ArticleGoogle Scholar
- Müller K, Merkle P, Tech G, Wiegand T: 3D video formats and coding methods. In Proceedings of the 17th IEEE International Conference on Image Processing (ICIP): 26–29 Sept. 2010. Hong Kong; 2010:2389-2392.View ArticleGoogle Scholar
- Stoykova E, Alatan A, Benzie P, Grammalidis N, Malassiotis S, Ostermann J, Piekh S, Sainov V, Theobalt C, Thevar T, Zabulis X: 3D time-varying scene capture technologies - a survey. IEEE Trans. Circuits Syst. Video Technol 2007, 17(11):1568-1586.View ArticleGoogle Scholar
- Benzie P, Watson J, Surman P, Rakkolainen I, Hopf K, Urey H, Sainov V, von Kopylow C: A survey of 3DTV displays: techniques and technologies. IEEE Trans. Circuits Syst. Video Technol 2007, 17(11):1647-1658.View ArticleGoogle Scholar
- Akar GB, Tekalp AM, Fehn C, Civanlar MR: Transport methods in 3DTV - a survey. IEEE Trans. Circuits Syst. Video Technol 2007, 17(11):1622-1630.View ArticleGoogle Scholar
- Mohib H, Swash MR, Sadka AH: Multi-view video delivery over wireless networks using HTTP. In Proceedings of International Conference on Communications, Signal Processing, and Their Applications: 12-14 Feb. 2013. Sharjah; 2013:1-5.Google Scholar
- Schierl T, Narasimhan S: Transport and storage systems for 3-D video using MPEG-2 systems, RTP, and ISO file format. Proc. IEEE 2011, 99(4):671-683.View ArticleGoogle Scholar
- Yen HH: Power-aware, bandwidth-aware and video-quality-aware cooperative routing algorithm for 3D video transmission in wireless networks. In Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PacRim): 23-26 Aug. 2011. Victoria, BC; 2011:470-475.View ArticleGoogle Scholar
- Gürler CG, Tekalp AM: Peer-to-peer system design for adaptive 3D video streaming. IEEE Commun. Mag 2013, 51(5):108-114.View ArticleGoogle Scholar
- Video Trace Library , Access date: 20 July 2013 http://trace.eas.asu.edu
- Seeling P, Reisslein M, Kulapala B: Network performance evaluation using frame size and quality traces of single-layer and two-layer video: a tutorial. IEEE Commun. Surv. Tutorials 2004, 6(3):58-78.View ArticleGoogle Scholar
- Seeling P, Fitzek FHP, Reisslein M: Video Traces for Network Performance Evaluation. Springer, Dordrecht; 2007.Google Scholar
- Seeling P, Reisslein M: Video transport evaluation with H.264 video traces. IEEE Commun. Surv. Tutorials 2012, 14(4):1142-1165.View ArticleGoogle Scholar
- Van Der Auwera G, David PT, Reisslein M: Video traffic analysis of H.264/AVC and extensions: single-layer statistics. Arizona State University, Technical report; 2007.Google Scholar
- Lakshman TV, Ortega A, Reibman AR: VBR video: tradeoffs and potentials. Proc. IEEE 1998, 86(5):952-973. 10.1109/5.664282View ArticleGoogle Scholar
- JSVM Reference Software Obtained at cvs-d:pserver:jvtuser@garcon.ient. rwth-aachen.de:/cvs/jvtcheckoutjsvm, Access date: 8 Aug. 2014Google Scholar
- JMVC Reference Software Obtained at cvs-d:pserver:jvtuser@garcon.ient. rwth-aachen.de:/cvs/jvtcheckoutjmvc, Access date: 8 Aug. 2014Google Scholar
- Sheluhin O, Smolskiy S, Osin A: Self-Similar Processes in Telecommunications. New York; 2007.View ArticleGoogle Scholar
- Riedi RH, Lévy Véhel J: TCP traffic is multifractal: a numerical study, Research report 3129, Inria Rocquencourt. 1997.Google Scholar
- de Godoy Stênico JW, Ling LL: A new binomial conservative multiplicative cascade approach for network traffic modeling. In Proceedings of IEEE 27th International Conference on Advanced Information Networking and Applications (AINA): 25-28 March 2013. Barcelona; 2013:794-801.View ArticleGoogle Scholar
- Dang TD, Molnár S, Maricza I: Capturing the complete multifractal characteristics of network traffic. Global Telecommunications Conference, GLOBECOM IEEE: 17-21 Nov.2002 2002, 2355-2359.View ArticleGoogle Scholar
- Evertsz A, Mandelbrot B: Multifractal measures. In Chaos and Fractals. Edited by: Peitgen H, Jürgens H, Andrews P. Springer, New York; 1992:849-881.Google Scholar
- Feder J: Fractals. Springer, New York; 1988.View ArticleMATHGoogle Scholar
- Murali P, Krishna VMG, Desai UB: Modelling and control of broad band traffic using multiplicative multifractal cascades. Sadhana, J. Indian Acad. Sci 2002, 27(6):699-723.Google Scholar
- Cosmas J, Loo J, Aggoun A, Tsekleves E: Matlab traffic and network flow model for planning impact of 3D applications on networks. In Proceedings of IEEE Int. Symp. on Broadband Multimedia Systems and Broadcasting: 24-26 March 2010. Shanghai; 2010:1-7.View ArticleGoogle Scholar
- Manap N, Di Caterina G, Soraghan J: Low cost multi-view video system for wireless channel. In Proceedings of IEEE 3DTV Conference:4–6 May 2009. Potsdam; 2009:1-4.Google Scholar
- Devi UC, Kalle RK, Kalyanaraman S: Multi-tiered, burstiness-aware bandwidth estimation and scheduling for VBR video flows. IEEE Trans. Netw. Serv. Manag 2013, 10(1):29-42.View ArticleGoogle Scholar
- Feng W, Rexford J: Performance evaluation of smoothing algorithms for transmitting prerecorded variable-bit-rate video. IEEE Trans. Multimedia 1999, 1(3):302-313. 10.1109/6046.784468View ArticleGoogle Scholar
- Hsu C-H, Hefeeda M: On statistical multiplexing of variable-bit-rate video streams in mobile systems. Proceedings of the 17th ACM International Conference on Multimedia 2009, 411-420.Google Scholar
- Van Der Auwera G, Reisslein M: Implications of smoothing on statistical multiplexing of H.264/AVC and SVC video streams. IEEE Trans. Broadcasting 2009, 55(3):541-558.View ArticleGoogle Scholar
- Raghuveera T, Easwarakumar K: An efficient statistical multiplexing method for H.264 VBR video sources for improved traffic smoothing. Int. J. Comput. Sci. Inf. Technol 2010, 2(2):51-62.Google Scholar
- Gürler G, Görkemli B, Saygili G, Tekalp AM: Flexible transport of 3-D video over networks. Proc. IEEE 2011, 99(4):694-707.View ArticleGoogle Scholar
- Vetro A, Tourapis AM, Müller K, Chen T: 3D-TV content storage and transmission. IEEE Trans. Broadcasting 2011, 57(2):384-394.View ArticleGoogle Scholar
- Peitgen H, Jürgens H, Saupe D: Chaos and Fractals. Springer, New York; 1992.View ArticleMATHGoogle Scholar
- Legrand P, Véhel JL: Signal and image processing with Fraclab. In Thinking in Patterns: Fractals and Related Phenomena in Nature. Edited by: Novak M. World Scientific, Singapore; 2003:321-322.Google Scholar
- Véhel JL, Tricot C: On various multifractal spectra. Prog. Probability 2004, 57(2004):23-42.MathSciNetMATHGoogle Scholar
- Reljin I, Samcovic A, Reljin B: H.264/AVC video compressed traces: multifractal and fractal analysis. EURASIP J. Adv. Signal Process 2006, 2006(75217):1-13.View ArticleGoogle Scholar
- Chhabra A, Meneveau C, Jensen V, Sreenivasan K: Direct determination of the f( α ) singularity spectrum and its application to fully developed turbulence. Phys. Rev. A 1989, 40(9):5284-5294. 10.1103/PhysRevA.40.5284View ArticleGoogle Scholar
- Strogatz SH: Nonlinear Dynamics and Chaos. Westview Press, Cambridge, Massachusetts; 2001.Google Scholar
- Oh S, Kulapala B, Richa AW, Reisslein M: Continuous-time collaborative prefetching of continuous media. IEEE Trans. Broadcasting 2008, 54(1):36-52.View ArticleGoogle Scholar
- Linawati, Sastra NP: Statistical multiplexing strategies for self-similar traffic. In IFIP International Conference on Wireless and Optical Communications Networks:5–7May 2008. Surabaya; 2008:1-5.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.