% bare_jrnl_transmag.tex
%% V1.4
%% 2012/12/27
%% by Michael Shell
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%% This is a skeleton file demonstrating the use of IEEEtran.cls
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% However, the above trick will not work if both optional arguments of
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% There should be no need to do such things with IEEEtran.cls V1.6 and later.
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% to submit to, of course. )


% correct bad hyphenation here
\hyphenation{op-tical net-works semi-conduc-tor}


\begin{document}
%
% paper title
% can use linebreaks \\ within to get better formatting as desired
% Do not put math or special symbols in the title.
\title{REPORT: Analysis of the influence of different artifacts in the video content from three experiments.}

% author names and affiliations
% transmag papers use the long conference author name format.

\author{\IEEEauthorblockN{Alexandre Fieno da Silva\IEEEauthorrefmark{1},
Judith A. Redi\IEEEauthorrefmark{2}, and
Myl\`{e}ne C.Q. Farias\IEEEauthorrefmark{1}}
\IEEEauthorblockA{\IEEEauthorrefmark{1}University of Brasilia, Campus Universitario Darcy Ribeiro, Brasilia - DF, Brazil, 70919-970}
\IEEEauthorblockA{\IEEEauthorrefmark{2}Delft University of Technology, Mekelweg 4, Delft, The Netherlands, 2628 CD}}
%%%%\thanks{Manuscript received December 1, 2012; revised December 27, 2012.
%%%%Corresponding author: A. F. Silva (email: alexandrefieno@gmail.com).}}

% The paper headers
%%%%\markboth{Journal of \LaTeX\ Class Files,~Vol.~11, No.~4, December~2012}%
%%%%%{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}
% The only time the second header will appear is for the odd numbered pages
% after the title page when using the twoside option.
%
% *** Note that you probably will NOT want to include the author's ***
% *** name in the headers of peer review papers.                   ***
% You can use \ifCLASSOPTIONpeerreview for conditional compilation here if
% you desire.


% If you want to put a publisher's ID mark on the page you can do it like
% this:
%\IEEEpubid{0000--0000/00\$00.00~\copyright~2012 IEEE}
% Remember, if you use this you must call \IEEEpubidadjcol in the second
% column for its text to clear the IEEEpubid mark.



% use for special paper notices
%\IEEEspecialpapernotice{(Invited Paper)}


% for Transactions on Magnetics papers, we must declare the abstract and
% index terms PRIOR to the title within the \IEEEtitleabstractindextext
% IEEEtran command as these need to go into the title area created by
% \maketitle.
% As a general rule, do not put math, special symbols or citations
% in the abstract or keywords.
\IEEEtitleabstractindextext{%
\begin{abstract}
  In this project, we are interested in understanding the impact of a set of artifacts on the quality of video, their relationship with the content and to determine their impact on visual quality perception. The set of artifacts used in this study was chosen among those most perceptually relevant for digital video applications (e.g. blockiness, blurriness, packet-loss etc). We first plan to perform a series of psychophysical experiments on visual quality perception. These experiments will determine the impact on quality first of a set of spatial and temporal artifacts by themselves, and later of their combinations, taking into account different content with varying spatial and temporal characteristics.
\end{abstract}

% Note that keywords are not normally used for peerreview papers.
\begin{IEEEkeywords}
  Quality of video, visual quality perception, blockiness, blurriness and packet loss.
\end{IEEEkeywords}}

% make the title area
\maketitle

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% here as \IEEEdisplaynontitleabstractindextext when the compsoc
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% For peer review papers, you can put extra information on the cover
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\section{Introduction}
% The very first letter is a 2 line initial drop letter followed
% by the rest of the first word in caps.
%
% form to use if the first word consists of a single letter:
% \IEEEPARstart{A}{demo} file is ....
%
% form to use if you need the single drop letter followed by
% normal text (unknown if ever used by IEEE):
% \IEEEPARstart{A}{}demo file is ....
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%%%%\IEEEPARstart{T}{his} demo file is intended to serve as a ``starter file''
%%%%for IEEE journal papers produced under \LaTeX\ using
%%%%IEEEtran.cls version 1.8 and later.

% You must have at least 2 lines in the paragraph with the drop letter
% (should never be an issue)
\IEEEPARstart{O}{ne} approach to design video quality metrics is to use artifact metrics. They have several advantages, for example simplicity and the ability to provide a good description of what is wrong (or right) with the video. But, in order to design good artifact metrics or any quality metric, it is necessary to have a good understanding of how artifacts affect quality. Therefore, it is important to study the characteristics of the most relevant artifacts present in digital video, what includes characteristics of annoyance, visibility, interactions among artifacts and content dependence. This way, we can use this data together with the data gathered previous to determine annoyance, visibility, and interaction for a diverse content.

In this experiment, we focus on $3$ particular artifacts that can be considered among the most relevant for digital video: blurriness, blockiness, and packet loss. We present these artifacts at several strengths, in several combinations and for seven original videos. In summary, we are interested in understanding "how" artifacts combine to determine annoyance and degradation in order to design better metrics.

\subsection{\textbf{Previous Experiments}}
\label{subsec:previousExp}


\subsubsection{Experiment 1}
In Experiment $1$ (\emph{Exp. $1$}), all videos were encoded using the $H.264\/AVC$ codec at a very high bitrate (approximately $120$ Mbps), with $8$ packets per frame. The coding process generated three sequences per video, with a Group of Pictures (GOP) size set to $4$, $8$ and $12$, respectively. Packet loss artifacts were then generated by dropping data packets from the bitstream~\cite{ISCAS13}. The packet loss ratios for all videos were $0.7\%$, $2.6\%$, $4.3\%$ and $8.1\%$. In total, it was generated $7$ videos x $3$ GOP x $4$ packet loss ratios = $84$ sequences. The $7$ pristine videos were added to the set, making a total of $13$ different settings per original video and resulting in $91$ test sequences~\cite{ISCAS13}.
\\
\subsubsection{Experiment 2}
Experiment $2$ (\emph{Exp. $2$}) is composed of two artifacts (blockiness and blurriness) considered the most salient present in digital videos~\cite{ICASSP05}. This experiment follows the ITU-T Recommendation P.$930$ that suggests to introduce blockiness only in regions where these artifacts are more visible. Thus, blockiness artifacts were produced by using the difference between the average of each $8 \times 8$ block and the average of the $24 \times 24$ surrounding area (which has the current $8 \times 8$ block as a center) to make each block stand out. Recommendation P.$930$ suggests the generation of blurriness with the use of a low-pass filter~\cite{ICASSP05}. Thus, blurriness artifacts were generated by applying a symmetric, two dimensional finite duration impulse response (FIR) low-pass filter to the digital image array. Different filters with varying cut-off frequencies could be used to control the amount of blurriness introduced. The blockiness and blurriness strengths for all videos were $0.4$, $0.6$ and $0.8$. In total, it was generated $7$ videos $\times$ $10$ blockiness and blurriness strengths = $70$ sequences. The $7$ pristine videos were added to the set, resulting in $77$ test sequences~\cite{SPIE03}.
\\
\subsubsection{Experiment 3}
In Experiment $3$ (\emph{Exp. $3$}) has been considered all original videos of the previous experiments. Also, it is composed of the three artifacts has been used in previous experiments (i.e., blockiness, blurriness and packet loss). In Exp. $3$ we are interested to understanding the impact on perceived quality of combinations of these artifacts and their relationship with the content. In total, we used $140$ test sequences and, details are given in the sub-section~\ref{subsec:Artifacts}.

\section{Experimental Setup}

\subsection{\textbf{Video Database}}
\label{subsec:VideoDatabase}
The video database used in this experiment was generated from seven high-definition videos (the same videos have been used in previous experiments). All them with $1280\times720$, $50$ fps and $10$ seconds duration. Representative frames of these originals are shown in Figure~\ref{fig:FirstFrame}.
\begin{figure}[h]
  \centering
  \includegraphics[width=3.5in]{../../Protocol/images/FirstFrames.jpg}
  \caption{First frame of the sequences included in Experiment.}
  \label{fig:FirstFrame}
\end{figure}

\subsection{\textbf{Artifacts}}
\label{subsec:Artifacts}
New versions of each original video were generated using different combinations of artifacts. To generate test sequences, we generate blockiness, blurriness and packet loss artifacts and combine these artifacts at different proportions and strengths. In summary, the following parameters are used to generate the test sequences:

\begin{itemize}
    \item $7$ original videos Basketball, Romeo and Juliet, Park Run, Cactus, Park Joy, Barbecue and Into Trees;
    \item $4$ different strengths of blockiness and blurriness: $0.4$, $0.6$ and $0.8$;
    \item $3$ different packet loss ratios: $0.7\%$, $2.6\%$, $4.3\%$ and $8.1\%$.
\end{itemize}

Due to a large number of combined test videos (over $200$ videos) makes it impossible to perform an experiment in a reasonable amount of time. Therefore, a reduction of the number of test sequences was achieved by selecting a subset of the original group. In this process, we eliminated most of the sequences already used in the two previous experiment already performed. We also reduced the number of parameters considering the results of the previous experiments. In summary, to select the test sequences we considered the following:

\begin{enumerate}
    \item Total time necessary to execute the overall experiment;
    \item The results of two previous experiments showed that the mean annoyance and the mean strength scores are ​​significantly different for packet loss ratios of $0.7$ and $8.1$.
    \item Among the blockiness and blurriness strength values considered in the previous experiments, we chose only the strength $0.4$ and $0.6$, which were considered to be more representative of these artifacts.
\end{enumerate}

\begin{table}[!htpb]
  \caption{Combinations of the packet loss ratio ($0.7$ and $8.1$) with blockiness and blurriness (strengths $0.4$ and $0.6$) artifacts.}
  \label{tab:PLBlockBlur}
  \centering
  % redefinindo o espaçamento das colunas
  \begin{tabular}{|c|c|c|c|}\hline
    Combination  & Packet-Loss & Blocky & Blurry  \\ \hline
            $1$  &   $0.0$     & $0.6$  & $0.0$   \\ \hline
            $2$  &   $0.0$     & $0.0$  & $0.6$   \\ \hline
            $3$  &   $8.1$     & $0.0$  & $0.0$   \\ \hline
            $4$  &   $0.7$     & $0.0$  & $0.4$   \\ \hline
            $5$  &   $8.1$     & $0.0$  & $0.4$   \\ \hline
            $6$  &   $0.7$     & $0.0$  & $0.6$   \\ \hline
            $7$  &   $8.1$     & $0.0$  & $0.6$   \\ \hline
            $8$  &   $0.7$     & $0.4$  & $0.0$   \\ \hline
            $9$  &   $8.1$     & $0.4$  & $0.0$   \\ \hline
           $10$  &   $0.7$     & $0.4$  & $0.4$   \\ \hline
           $11$  &   $8.1$     & $0.4$  & $0.4$   \\ \hline
           $12$  &   $0.7$     & $0.4$  & $0.6$   \\ \hline
           $13$  &   $8.1$     & $0.4$  & $0.6$   \\ \hline
           $14$  &   $0.7$     & $0.6$  & $0.0$   \\ \hline
           $15$  &   $8.1$     & $0.6$  & $0.0$   \\ \hline
           $16$  &   $0.7$     & $0.6$  & $0.4$   \\ \hline
           $17$  &   $8.1$     & $0.6$  & $0.4$   \\ \hline
           $18$  &   $0.7$     & $0.6$  & $0.6$   \\ \hline
           $19$  &   $8.1$     & $0.6$  & $0.6$   \\ \hline
    \end{tabular}
\end{table}

Thus, taking into account these choices, they resulted into $19$ combinations (Table~\ref{tab:PLBlockBlur}) $\times ~7$ (video versions) $+ ~7$ original videos $= 140$ videos. This is a reasonable number of sequences which made it possible to run the experiment in the allocated time of $50$ minutes.

\subsection{\textbf{Methodology}}
\label{subsec:Methodology}
For all experimental session, subjects are requested to score a set of test videos with different combinations of artifacts. The number of observers was chosen in accordance with ITU Recommendations~\cite{ITU98}. A Single Stimulus setup with implicit reference~\cite{ITU98} was adopted for the task. The experiment is running in a room with constant illumination of approximately $70$ lux. Each subject watches the stimuli (test sequences) on a $23$ inches LED monitor of resolution $1360\times768$. The distance between the subject's eyes and the video monitor is $3$ times the monitor screen's height.

Subjects are seated straight ahead of the monitor, centered at or slightly  below the eye height for most subjects. A chinrest is used to guarantee a constant distance between the subject's eyes and the monitor. A SensoMotoric Instruments iView RED Eye Tracker was used throughout the experiment to record the eye-movements of the participants (The eye tracker had a sampling rate of $50/60$Hz, a pupil tracking resolution of $0.1^{\circ}$ and a gaze position accuracy of $0.5 - 1^{\circ}$). The user interface for the experiment is implemented using the \emph{Neurobehavioral Systems} software Presentation.

All subjects are asked to watch the test sequences and indicate whether they perceived any impairment in the videos; if so, they were asked to enter how annoying the artifacts were on a continuous annoyance scale ranging between $0$ to $100$. During the experiment, the experimenter guides the subject through all sessions (calibration, free viewing, training, practice and a main experiment). Table~\ref{tab:Exp} show the summary of the settings of each experiment.

\begin{table}[!htpb]
    % increase table row spacing, adjust to taste \renewcommand{\arraystretch}{1.3}
    % if using array.sty, it might be a good idea to tweak the value of
    %\extrarowheight as needed to properly center the text within the cells
    \caption{Summary overall experiments}
    \label{tab:Exp}
    \centering
    % Some packages, such as MDW tools, offer better commands for making tables
    % than the plain LaTeX2e tabular which is used here.
    \begin{tabular}{|c||c|c|c|}
    \hline
             Settings     & Exp. $1$   & Exp. $2$   & Exp. $3$   \\  \hline
Numbers of Test Sequences &   $91$     &   $77$     &  $140$     \\  \hline
      Numbers of Subjects &   $15$     &   $16$     &  $23$      \\  \hline
            Calibration   &   $-$      & \emph{Yes} & \emph{Yes} \\  \hline
           Free Viewing   & \emph{Yes} & \emph{Yes} & \emph{Yes} \\  \hline
               Training   & \emph{Yes} & \emph{Yes} & \emph{Yes} \\  \hline
        Practice Trials   & \emph{Yes} & \emph{Yes} & \emph{Yes} \\  \hline
        Main Experiment   & \emph{Yes} & \emph{Yes} & \emph{Yes} \\  \hline
            Packet Loss   & \emph{Yes} &   $-$      & \emph{Yes} \\  \hline
             Blockiness   &   $-$      & \emph{Yes} & \emph{Yes} \\  \hline
             Blurriness   &   $-$      & \emph{Yes} & \emph{Yes} \\  \hline
    \end{tabular}
\end{table}

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\section{Data Analysis}
We used the standard methods~\cite{ITU98} for analyzing the annoyance judgments provided by the test subjects. We first computed Mean Opinion Score (MOS) for each test sequence. MOS is calculated by averaging the annoyance levels over all observers for each video. In this report, values of MOS are treated as \emph{MOS1}, \emph{MOS2} and \emph{MOS3} (i.e., MOS of the \emph{Exp.1}, \emph{Exp.2} and \emph{Exp.3}, respectively). To test the relationship among the MOS values we used the Pearson correlation ($r$). To determine the significant effects between MOS's values we used a non-parametric T-Test. We also are analysing only where settings (artifacts) are the same among the experiments:
 \begin{itemize}
   \item Case $1$: from Exp. $1$ until $3$ considering only original videos;
   \item Case $2$: between Exp. $1$ and Exp. $3$, considering only the $8.1\%$ ratio of packet loss artefact;
   \item Case $3$: between Exp. $2$ and Exp. $3$, considering only the $0.6$ strengths of blockiness and blurriness artifacts (isolated).
 \end{itemize}

\subsection{\textbf{Case $1$}}
\label{subsec:case1}
We report the MOS's computed across all $7$ versions of video and all experiments, as shown in Figure~\ref{fig:AllMOS}. It is interesting to notice how the MOS' changes among the experiments: the MOS of the experiments $1$ until $3$ are $1.43$, $4.92$ and $0.80$, respectively. The Pearson linear correlation between $MOS1$ and $MOS3$ was $0.382$, $-0.0.26$ between $MOS1$ and $MOS2$, and $0.008$ between $MOS2$ and $MOS3$ for original test sequences.
\begin{figure}[h]
  \centering
  \includegraphics[width=3.7in]{MOS---AllExperiments.jpg}
  \caption{Mean Annoyance Values averaged across all the original versions of each video.}
  \label{fig:AllMOS}
\end{figure}

\subsection{\textbf{Case $2$}}
\label{subsec:case2}
In this case, we report the $MOS1$ and $MOS3$ computed to packet loss artefact across all $7$ versions of video, as shown in Figure~\ref{fig:pckMOS}. It is possible to notice that $MOS1 = 72.85$ is very higher than $MOS3 = 37.99$ with $r=.536$ and $p<.05$.
\begin{figure}[h]
  \centering
  \includegraphics[width=3.7in]{MOS---pckExp.jpg}
  \caption{Mean Annoyance Values averaged across of the packet loss versions of each video.}
  \label{fig:pckMOS}
\end{figure}

\subsection{\textbf{Case $3$}}
\label{subsec:case3}
Two different analyses were made between $MOS2$ and $MOS3$ across all $7$ versions of video: first, we report the MOS's computed only to blockiness artefact and next, we report the MOS's computed only to blurriness artefact. Figure~\ref{fig:bloMOS} shows a slightly difference between $MOS2$ and $MOS3$ to Park Joy, Into Tree and Park Run videos, but this difference increased when to Romeo \& Juliet, Cactus, Basketball and Barbecue videos. Spearman's correlation coefficient has showed that there is a moderate and positive correlation ($\rho = .536$) between them. Although, there are statistically significant different only to Romeo \& Juliet, Cactus and Basketball videos ($p<.05$).
\begin{figure}[h]
  \centering
  \includegraphics[width=3.7in]{MOS---bloExp.jpg}
  \caption{Mean Annoyance Values averaged across of the blockiness versions of each video.}
  \label{fig:bloMOS}
\end{figure}

Next, it is considered blurriness artefact. As shown in Figure~\ref{fig:bluMOS} there is a slightly difference between $MOS2$ and $MOS3$ to Park Joy, Into Tree, Cactus, Basketball and Barbecue videos increasing this different to Park Run and Romeo \& Juliet videos. Despite of strong correlation ($\rho = .857$) between them, there was not statistically significant different ($p>.05$).
\begin{figure}[h]
  \centering
  \includegraphics[width=3.7in]{MOS---bluExp.jpg}
  \caption{Mean Annoyance Values averaged across of the blurriness versions of each video.}
  \label{fig:bluMOS}
\end{figure}

\section{Data Analysis across all Experiments}
During the VARIUM tests~\cite{VARIUM13} were produced $3$ data sets spread nearly equally across settings where each experiments took into account different kinds of artifacts. Each experiment was defined by its artefact used. Specifics of the testing, such as the $H.264$ encoding parameters, can be found in~\cite{ISCAS13,ICME03,SPIE03}. The subjective MOS scores were tested by correlation the results across the experiments: a set of video sequences was common across the experiments in test. Spearman correlations of each experiment group were $.89$, $.83$ and $.87$ between $Exp.1$ and $Exp.3$ (packet loss), $Exp.2$ and $Exp.3$ (blockiness) and $Exp.2$ and $Exp.3$ (blurriness), respectively. One Spearman correlation of $.91$ was found between $Exp.2$ and $Exp.3$ (blockiness and blurriness).




\section{Conclusion}
The conclusion goes here.



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%%%%\section{Proof of the First Zonklar Equation}
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\bibitem{VARIUM13}
  Farias,~M.~C.~Q.; Heynderickx,~I.; Bruno,~L.~M.~E.; Redi,~J.~A., \emph{Visual Artifacts Interference Understanding and Modeling (VARIUM)}. In: Seventh International Workshop on Video Processing and Quality Metrics for Consumer Electronics, 2013, Scottsdale.

\bibitem{ISCAS13}
  Redi,~J.~A.; Heynderickx,~I.; Bruno,~L.~M.~E.; Farias,~M.~C.~Q., \emph{On the impact of packet-loss impairments on visual attention mechanisms}. In: ISCAS, pp. 1107-1110. IEEE, 2013.

\bibitem{ICME03}
  Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Perceptual contributions of blocky, blurry and noisy artifacts to overall annoyance}. In: Proc. IEEE International Conference on Multimedia \& Expo, Baltimore, MD, USA, 2003, Vol. 1, pp. 529–532.

\bibitem{SPIE03}
  Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Some Properties of Synthetic Blocky and Blurry Artifacts}. In: Proceedings of the SPIE, Human Vision and Electronic Imaging VIII, Vol. 5007, pp. 128-136 (2003).

\bibitem{SPIE05}
  Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Perceptual analysis of video impairments that combine blocky, blurry, noisy, and ringing synthetic artifacts}. In: Proceedings of the SPIE, Human Vision and Electronic Imaging VIII, Vol. 5666, pp. 107-118 (2005).

\bibitem{ICASSP04}
    Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Detectability and Annoyance of Synthetic Blurring and Ringing in Video Sequences}. In: International Conference on Acoustics, Speech, and Signal Processing - ICASSP, Vol. 3, pp. 697-700, 2004.

\bibitem{SSDT02}
  Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Detectability and Annoyance of Synthetic Blocky and Blurry Video Artifacts}. In: Journal Sid Symposium Digest of Technical Papers, Vol. 33, No. 1, 2002.

\bibitem{ICASSP05}
  Farias,~M.~C.~Q.; Foley,~J.~M.; Mitra,~S.~K., \emph{Detectability and annoyance of synthetic blockiness, blurriness, noisiness, and ringing in video sequences}. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, Vol. 2, pp. 553–556, 2005.

\bibitem{ITU98}
  International Telecommunication Union, \emph{ITU-T Recommendation BT.500-8: Methodology for the subjective assessment of the quality of television pictures}. 1998.

\bibitem{ICIP05}
  Farias,~M.~C.~Q.; Mitra,~S.~K., \emph{No-reference video quality metric based on artifact measurements}. In: ICIP, IEEE, pp. 141-145, 2005.

\bibitem{SPSS04}
  Landau,~S.; Everitt,~B.~S., \emph{A Handbook of Statistical Analyses Using SPSS}. ISBN: 9781584883692, Statistics Chapman \& Hall/CRC, Taylor \& Francis, 2004.

\bibitem{Statist09}
  Montgomery,~D.,~C., \emph{Introduction To Statistical Quality Control}. ISBN: 9780470233979, John Wiley \& Sons, Incorporated, 2009.

\bibitem{IMGPRO10}
  Seshadrinathan,~K.; Soundararajan,~R.; Bovik,~A.~C.; Cormack,~L.~K., \emph{Study of Subjective and Objective Quality Assessment of Video}. In: IEEE Transactions on Image Processing, Vol. 19, No. 6, Piscataway, NJ, USA, 2010.
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