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Fix abreviations and equations referencing

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jfmcarreira
2018-12-10 19:19:32 +00:00
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043_EndToEndRobustSystem/EndToEndRobustSystem.tex
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@@ -10,17 +10,11 @@
\vspace*{\fill}
\section{Introduction}
\glsunset{hevc}
\glsreset{ec}
\glsreset{rd}
\glsreset{el}
\glsreset{nn}
The effectiveness of different error resilience techniques is rather variable depending on the video content, the transmitted bitrate, network conditions, among others.
Therefore, it is not straightforward to select a single technique capable of guaranteeing the highest efficiency for every possible scenario and also for the whole range of networking conditions.
This chapter addresses this problem by proposing an adaptive error robustness scheme that optimises the efficiency of the techniques previously described in Chapters~\ref{Chapter:ErrorResilienceTechniques}~and~\ref{Chapter:ConcealmentAwareTechniques}.
At the encoder-side, a pre-trained neural network predicts the video stream that is able to achieve the highest decoded quality for particular loss cases, among various video streams encoded with different levels of compression efficiency and error robustness.
The stream selection is based on different input parameters that include information from the video signal, the coded stream and the transmission network.
The stream selection is based on different input parameters that in\textsl{}clude information from the video signal, the coded stream and the transmission network.
Using this approach the overall performance of the proposed adaptive method significantly increases, achieving a better trade-off between compression efficiency and error robustness.
This chapter is organised as follows.
@@ -186,9 +180,6 @@ This results in a total of \NumCases test conditions.
\subsection{Input parameters}
\glsreset{si}
\glsreset{ti}
A set of different parameters is used to predict the optimal stream that achieves the highest quality in specific network conditions.
Based on these inputs the proposed scheme should be able to select the appropriate robust coding stream that should be transmitted.
The parameter set characterises three different aspects of the video transmission system; (i) input video (two parameters), (ii) video stream (three parameters) and (iii) transmission network (one parameter).
@@ -252,8 +243,6 @@ Since both \textit{AVG\_MV} and \textit{OPT\_FLOW} represent similar informatio
\subsection{Neural network}
\glsreset{nn}
A \ac{nn} is a computational model based on the structure and functions of biological neural networks.
%In terms of computer science, \acs{nn} work similarly to a human nervous system for receiving, processing, and transmitting information between neurons.
Mainly, a \ac{nn} consists of three different layers: the input layer which receives data from an external environment, the hidden layer which process the inputs and an output layer~\cite{NeuralNetworks1994}.
@@ -321,8 +310,6 @@ The \ac{nn} is able to achieve a high accuracy for \SMEC and \SMEXPELTWEN stream
This reveals that the \ac{nn} used in the proposed method is able to predict the coded stream which leads to the highest video quality across different conditions with acceptable accuracy.
The results from the validation set, shown in Table~\ref{Table6:ConfusionVal}, reveal that the \ac{nn} training did not over-fit, since it shows similar accuracy as the results shown in Table~\ref{Table6:ConfusionTraining}.
\begin{table}[tb]
\centering
\caption{Confusion matrix of the validation set.}
@@ -452,8 +439,6 @@ These results also confirm that the proposed method is able to select the optima
The proposed method achieves a quality gain up to 4.46 dB in comparison with the reference \ac{hevc}, outperforming the quality achieved with the stream \SMEXPELTWEN (i.e., 4.19 dB).
This confirms the advantage of the proposed method to adaptively select among different streams with different levels of error protections.
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