29. Page 80, Figure 5.1: it is useful to also show bars of r2 side by side with r1.
Figure 5.1 was modified to add a new bar to represent the usage ratio of r2. Text describing the figure was revised accordingly. 30. Page 84, in (5.2): explain the meaning of symbol “S”. “S” corresponds to the totalk number of reference frames. It is now introduced in paragraph 3 of Section 5.4. 31. Page 84, after (5.2): change “in this case” to “where”. After (5.2) the text was revised accordingly. 32. Page 84, in (5.4): explain the meaning of symbol . The is the Lagrangian cost obtained from D and R. This is used to jointly minimise both the coding distortion and the generated bitrate in a video encoder, achieving a trade-off between both. The sentence after the equation was revised to mention this information. 33. 34. Page 89, “This weight varies …”: which is the weight? Please see the comment and revise the sentence. The mention to alpha parameter was removed from the previous sentence. This way it is easier to understand which weight is mention in this sentence. The description of alpha was moved to the next sentence. 35. P94, “Influence of parameter γ”: how do you select γ? Do you have an algorithm for the selection or optimised value for γ? The candidate needs to elaborate more about the selection of γ. The value of γ was empirical selected in our experiments. However, in order to evaluate the influence of the γ parameter in the performance of the proposed method extensive experiments for the different values were carried out. The last sentence of this paragraph was revised to elaborate on how it should be selected.
This commit is contained in:
@@ -15,9 +15,9 @@
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\vspace*{\fill}
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\section{Introduction}
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This chapter proposes a new two-stage approach to improve the error robustness of \ac{hevc} streaming, by reducing temporal error propagation in case of frame loss.
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This chapter proposes a new two-stage approach to improving the error robustness of \ac{hevc} streaming, by reducing temporal error propagation in case of frame loss.
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This is accomplished by reducing the prediction mismatch that occurs at the decoder after frame loss.
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The proposed approach comprises the following two stages: (i) at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimisation, which distributes the use of reference pictures throughout each frame, reducing the number of \ac{pu} that depend on a single reference; (ii) at the streaming stage, a \ac{mv} prioritisation algorithm, based on spatial dependencies, selects an optimal sub-set of \acs{mv} to be transmitted as side redundant information to reduce mismatched \ac{mv} predictions at the decoder.
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The proposed approach comprises the following two stages: (i) at the encoding stage, the reference pictures are dynamically selected based on constraining conditions and Lagrangian optimisation, which distributes the use of reference pictures throughout the frames, reducing the number of \acs{pu} that depend on a single reference; (ii) at the streaming stage, a \ac{mv} prioritisation algorithm, based on spatial dependencies, selects an optimal sub-set of \acs{mv} to be transmitted as side redundant information to reduce mismatched \ac{mv} predictions at the decoder.
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This chapter is organized as follows. Section~\ref{Section4:Problem} presents the problem definition and motivation for the method proposed in the chapter. Section~\ref{Section4:Architecture} provides an overview of the proposed method, describing the functional blocks that compose the two-stage approach for robust coding and streaming. Section~\ref{Section4:RefPicSel} describes the encoding stage of the proposed method, that is used to select the optimal reference picture for inter-prediction. Section~\ref{Section4:MVSelection} describes the streaming stage where a sub-set of \acs{mv} are selected to reduce the mismatch motion predictions. Section~\ref{Section4:Performance} provides a performance evaluation of the proposed method, illustrating the advantages of each stage and how they are combined to achieve superior error robustness in \ac{hevc} standard. Finally, Section~\ref{Section4:Summary} concludes the chapter.
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@@ -28,22 +28,22 @@ This chapter is organized as follows. Section~\ref{Section4:Problem} presents th
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\section{Problem definition}
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\label{Section4:Problem}
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As previously mentioned, when\ac{hevc} bitstreams are subjected to transmission losses, the decoded video signal incurs in significant degradation of both objective and subjective quality.
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As previously mentioned, when \ac{hevc} bitstreams are subject to transmission losses, the decoded video signal incurs in significant degradation of both objective and subjective quality.
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This is mostly due to the strong decoding dependencies imposed by the highly complex prediction modes, which are selected according to \ac{rd} optimisation criteria, which always assume error-free transmission~\cite{ROPE2007}.
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Therefore, high compression efficiency is achieved, but resilience to transmission errors is penalised.
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\begin{figure}[tb]
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\centering
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\input{./041_ImprovedErrorResilience/Results/AverageRefPicUsage.tex}
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\caption{Average usage of the reference frame 1 ($r_1$) for different sequences.}
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\caption{Average usage of the reference frame 1 ($r_1$) and 2 ($r_2$) for different sequences.}
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\label{FIG4:ReferenceUsageRatio}
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\end{figure}
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Highly efficient video encoders support the use of multiple reference frames for inter-frame prediction, which are selected based on minimisation of \ac{rd} cost that may lead to an unbalanced use of various reference frames.
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In order to analyse the distribution of the reference frames selected by the \ac{rd} decision method used in the reference \ac{hevc} encoder, different test sequences were encoded using two reference frames, referred to as $r_1$ and $r_2$, and their selection ratios were analysed.
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In this study, reference $r_1$ corresponds to the closest neighbouring frame and reference $r_2$ the second closest.
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Figure~\ref{FIG4:ReferenceUsageRatio} illustrates the average usage percentage of reference $r_1$ for different sequences.
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The remaining corresponds to those cases where reference $r_2$ is selected.
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Figure~\ref{FIG4:ReferenceUsageRatio} illustrates the average usage percentage of reference $r_1$ and $r_2$ for different sequences.
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%The remaining corresponds to those cases where reference $r_2$ is selected.
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These results show that, in general, reference $r_1$ is selected more often than $r_2$.
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This is mainly true for sequences with higher motion.
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For those sequences with lower motion, but high spatial complexity, e.g., BQSquare (see \ac{si} in Table~\ref{TableTestSequences}), both reference frames are equally (approximately) selected for temporal predictions, with $r_1$ being used by 58\% of cases and $r_2$ by 42\%.
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@@ -51,7 +51,7 @@ This reveals that, even though two references are used, the encoder mainly uses
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Therefore, if such reference frame is lost, a higher number of mismatched predictions will occur, inevitably leading to higher error propagation.
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Another coding technique that contributes to increase error propagation is the use of predictions in \ac{mv} coding~\cite{MotionCodingHEVC2011}, which also contributes to increase the total amount of spatial and temporal dependencies.
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As shown in Chapter~\ref{Chapter:HEVCErrorResilience}, temporally predicted \acs{mv} are responsible for a greater impact on error propagation, as they propagate errors in the \ac{mv} decoding across different time instances.
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As shown in Section~\ref{Section3:MotionVectorCodingTests}, temporally predicted \acs{mv} are responsible for a greater impact on error propagation, as they propagate errors in the \ac{mv} decoding across different time instances.
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To overcome these issues a two-fold approach was devised to increase the error robustness in video streaming by jointly considering two different stages: (i) while encoding live video, by selecting robust coding modes and (ii) after encoding, by adding robust features to the compressed streams. While in the former this is accomplished by optimising coding parameters and decisions to increase robustness, in the latter some type of compressed domain processing is used to increase error robustness of pre-encoded streams, e.g., error resilient transcoding. Inevitably, in both cases this is achieved at the expense of some loss in coding efficiency, therefore the challenge is to find an optimum trade-off between robustness and bitrate overhead.
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The proposed method is described in the remaining of this chapter.
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@@ -62,7 +62,7 @@ The proposed method is described in the remaining of this chapter.
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\section{Proposed two-stage architecture}
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\label{Section4:Architecture}
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The goal of the proposed method is to reduce the error propagation in decoded video when the received stream was incurred in frame loss.
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The goal of the proposed method is to reduce the error propagation in decoded video when the received stream incurs frame loss.
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The underlying idea is to constrain inter-frame prediction dependencies, in order to minimise the frame distortion $D_t$ given by
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\begin{equation}
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D_t = \tilde f_t - \hat f_t, \text{when } \tilde f_{t-1} \neq \hat f_{t-1},
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@@ -80,7 +80,7 @@ Note that the streaming stage is an independent and complementary subsystem of t
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\centering
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% \includegraphics[width=0.85\columnwidth]{041_ImprovedErrorResilience/Figures/BlockDiagram}
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\input{041_ImprovedErrorResilience/Figures/BlockDiagram.tikz}
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\caption{Functional blocks of the proposed method (\textsl{gray} blocks) in the context of the \ac{hevc} coding and streaming.}
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\caption{Functional blocks of the proposed method (blue blocks) in the context of the \ac{hevc} coding and streaming.}
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\label{fig:proposedMethod}
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\end{figure}
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@@ -93,7 +93,7 @@ This is equivalent to reduce the error propagation by means of two different mec
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\label{Section4:RefPicSel}
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The reference picture selection mechanism attempts to uniformly distribute the use of different reference pictures across the \ac{ctu} of a coded video frame.
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The reference picture selection mechanism attempts to uniformly distribute the use of different reference pictures across the \acs{ctu} of a coded video frame.
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In case of frame loss, the error propagation in the decoder is reduced because the number of \acs{ctu} predicted from the lost frame decreases and thus \ac{ec} benefits as more data is correctly decoded from other reference frames.
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The underlying principle of this method is to minimise the probability of using the same reference picture for neighbouring \acs{ctu}.
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@@ -112,28 +112,28 @@ However, such solution would not take into consideration coding efficiency, sinc
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\label{fig:checkerboardRefSelection}
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\end{figure}
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Therefore, a more efficient approach to improve error robustness was devised to dynamically choose the coding mode for each \ac{pu}, that ensures a balanced use of all possible reference frames $F=\{r_1,r_2,...r_S\}$ across the \acs{ctu} of the current frame $C$, also optimising the \ac{rd} cost.
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Therefore, a more efficient approach to improving error robustness was devised to dynamically choose the coding mode for each \ac{pu}, that ensures a balanced use of all possible reference frames $F=\{r_1,r_2,...r_S\}$, where $S$ is the total number of references, across the \acs{ctu} of the current frame $C$, also optimising the \ac{rd} cost.
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This leads to the overall goal of finding the optimum R-D coding mode for each \ac{pu}, as the one which tends to minimise the standard deviation of the reference frames use count $\mathbf{N}_C(r_i)$, considering all encoded \acs{pu} of the current frame $C$, as follows:
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\begin{equation}
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\sigma_C=std\Big(\mathbf{N}_C(r_i) \Big), i=1, 2,\cdots S.
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\sigma_C=std\Big(\mathbf{N}_C(r_i) \Big), i=1, 2,\cdots S,
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\label{eq:StdX}
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\end{equation}
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In this case $\sigma_C$ measures the deviation from the checkerboard solution, where $\sigma_C = 0$, since all possible reference frames $r_i\in F$ are uniformly used throughout the \acs{pu} of $C$.
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where $\sigma_C$ measures the deviation from the checkerboard solution, which corresponds to $\sigma_C = 0$, since all possible reference frames $r_i\in F$ are uniformly used throughout the \acs{pu} of $C$.
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To attain the goal defined above, the best coding mode for each \ac{pu} is chosen through a Lagrangian \ac{rd} optimisation that tends to minimise $\sigma_C$.
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This is done by penalising (or benefiting) the \ac{rd} cost of those coding modes which intend to use reference frames that have already been used more (or less) frequently than their average use in previous \acs{pu} of the current frame.
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For each \ac{pu}, this mechanism has the effect of reducing (or increasing) the probability of using each possible reference frame $r_i\in F$, according to the number of previous \acs{pu} that have used each one.
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Such approach allows to select the reference frames, by independently processing each \ac{pu} in order to minimise the global standard deviation given by~(\ref{eq:StdX}). Thus, the optimum coding mode $\phi^*$, selected from a set of possible coding modes $M$, is derived based on the following Lagrangian optimisation:
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\begin{align}
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\phi^* &= arg \min_{\phi \in M} J(\phi) \\
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J(\phi) &= \Big( D(\phi) + \lambda \times R(\phi) \Big) \times e^{W(r_{\phi})}
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\phi^* &= arg \min_{\phi \in M} J(\phi), \\
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J(\phi) &= \Big( D(\phi) + \lambda \times R(\phi) \Big) \times e^{W(r_{\phi})},
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\label{eq:Lagrangian}
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\end{align}
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where $D(\phi)$ and $R(\phi)$ are the distortion and rate, respectively, associated with the coding mode $\phi$.
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where $D(\phi)$ and $R(\phi)$ are the distortion and rate, respectively, associated with the coding mode $\phi$ and $J(\phi)$ is the trade-off between distortion and rate.
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The exponential weighting factor $e^{W(r_{\phi})}$ is used to penalise (or benefit) the Lagrangian cost of a given coding mode $\phi$ associated to reference frame $r_\phi$.
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$W(r_{\phi})$ is obtained according to the global and local deviation between the number of times that $r_\phi$ was used and the average use of all $r_i \in F$ across the \acs{pu} of current frame $C$, as follows,
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\begin{equation}\label{eq:KFactor}
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W(r_{\phi}) = \Big( \Delta_G(r_{\phi}) + \Delta_L(r_{\phi}) \Big)\times 2^{-T_{ID}} \times \gamma
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W(r_{\phi}) = \Big( \Delta_G(r_{\phi}) + \Delta_L(r_{\phi}) \Big)\times 2^{-T_{ID}} \times \gamma,
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\end{equation}
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where $T_{ID}$ is given by the temporal hierarchy in \ac{hevc}, and $\gamma$ is a parameter to control the slope of ${W(r_\phi)}$.
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The global and local deviations, $\Delta_G (r_\phi)$ and $\Delta_L (r_\phi)$, respectively, are computed as follows,
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@@ -151,13 +151,13 @@ Note that an exponential cost increase is used in (\ref{eq:Lagrangian}) in order
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\label{fig:weightfct}
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\end{figure}
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In (\ref{eq:KFactor}), the $\gamma$ constant controls the slope of $W(r_\phi)$, leading to higher weights as the value of $\gamma$ increases.
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In (\ref{eq:KFactor}), the constant $\gamma$ controls the slope of $W(r_\phi)$, leading to higher weights as the value of $\gamma$ increases.
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On the one hand a very high value for $\gamma$ leads to the checkerboard pattern in the reference picture usage, as previously described.
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On the other hand, a low value of $\gamma$ reduces the importance of the choice of the reference frames in the Lagrangian cost.
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Therefore, the impact of the proposed method on the coding efficiency can be controlled through $\gamma$. Another way of controlling the impact of the proposed method is the use of the $T_{ID}$. This allows using lower weights in higher temporal layers, since such frames cannot be used as references for others with lower temporal ID, thus having less impact on error propagation.
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Therefore, there is a reduction of the impact of the proposed method when hierarchical coding configuration is adopted without compromising the error robustness.
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As an example, Figure~\ref{fig:weightfct} illustrates the exponential weight used in the proposed method for different values of $\gamma$ and $T_{ID}=0$. The horizontal axis represents the sum of $\Delta_G(r_\phi)$~plus~$\Delta_L(r_\phi)$.
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As an example, Figure~\ref{fig:weightfct} illustrates the exponential weight used in the proposed method for different values of $\gamma$ and $T_{ID}=0$. The horizontal axis represents the sum of $\Delta_G(r_\phi)$~and~$\Delta_L(r_\phi)$.
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For instance, for $\gamma=5$, when a reference frame $r_\phi$ is used to predict 3 more \acs{ctu} ($\Delta_G(r_\phi) + \Delta_L(r_\phi)=3$) than the other reference frames (all \acs{pu} within those \acs{ctu} use the same reference), then the Lagrangian cost is increased by $50\%$ ($e^{W(r_{\phi})}\approx1.5$).
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@@ -193,8 +193,8 @@ Note that standard compatibility is maintained, since the proposed optimisation
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Since \ac{hevc} uses differential and predictive \ac{mv} coding, whenever a \ac{mv} is lost the subsequent ones will also be affected, until a refresh point (i.e., \ac{mv} coded on its own, without using \ac{mv} prediction) is reached to break the dependency chain.
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Thus, the amount of error propagation strongly depends on whether the \ac{tmvp} is used.
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In this streaming stage, to increase the number of possible refresh points and improve error robustness, a small number of \acs{mv} are selected to be transmitted as side information. The selection of these redundant \acs{mv} is based on a trade-off between the image area covered by such \acs{mv} and the number of bits to encode them.
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The best tradeoff corresponds to the \acs{mv} that cover the largest image area using the lowest amount of encoding bits.
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In the streaming stage, to increase the number of possible refresh points and improve error robustness, a small number of \acs{mv} are selected to be transmitted as side information. The selection of these redundant \acs{mv} is based on a trade-off between the image area covered by such \acs{mv} and the number of bits to encode them.
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The best trade-off corresponds to the \acs{mv} that cover the largest image area using the lowest amount of encoding bits.
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\def\MVDEPA{\mbox{(a)}\xspace}
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\def\MVDEPB{\mbox{(b)}\xspace}
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@@ -206,7 +206,7 @@ While the \ac{mv} of \ac{pu} \MVDEPA is used to predict a total of four other \a
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Thus, in case of data loss (e.g., frame $f_{-1}$), the \ac{mv} of \ac{pu} \MVDEPA has more impact on error propagation to the frame $f_{0}$ and consequently quality degradation than the \ac{mv} of \ac{pu} \MVDEPB, because of its higher number of dependent \acs{mv}.
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For this reason, in the \ac{mv} selection process, the proposed method assigns higher importance to the \ac{mv} of \ac{pu} \MVDEPA than \ac{pu} \MVDEPB.
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Moreover, in Figure~\ref{fig:mvdependencies} it can be observed that, although the number of \acs{mv} predicted from the \acs{mv} of \ac{pu} \MVDEPC is higher than \ac{pu} \MVDEPA (6 and 4 \acs{mv}, respectively),
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the image area that depends on the former is smaller that the latter. This is due to the variable size of \acs{pu}.
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the image area that depends on the former is smaller than the latter. This is due to the variable size of \acs{pu}.
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Therefore, the image area covered by all dependent \acs{pu} is separately considered in the \ac{mv} selection process.
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\begin{figure}[tb]
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@@ -216,12 +216,12 @@ Therefore, the image area covered by all dependent \acs{pu} is separately consi
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\label{fig:mvdependencies}
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\end{figure}
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Based on the evidence mentioned above, the best \acs{mv} to be encoded as side information for each frame are selected through the following procedure:
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Based on the evidences mentioned above, the best \acs{mv} to be encoded as side information for each frame are selected through the following procedure:
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\begin{enumerate}
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\item
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the \acs{mv} with temporal dependencies are firstly selected as the most important set: $V$ = \{\emph{\ac{mv}: \ac{mv} is temporally dependent from others}\}.
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\item
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the sub-set of $MVs \in V$ are ranked according to the number of spatial dependencies associated to each one.
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the elements of $MVs \in V$ are ranked according to the number of spatial dependencies associated to each one.
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\item
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the optimal sub-set $U \subset V$ is found as the best trade-off between the image area covered by the dependent \acs{pu} and bitrate overhead, i.e., the one that maximises the cost function in (\ref{eq:MVSelectOpt}).
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\end{enumerate}
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@@ -229,7 +229,6 @@ the optimal sub-set $U \subset V$ is found as the best trade-off between the ima
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This can be formulated as follows. Considering $V$ the ordered set of \acs{mv} encoded for the current frame, i.e., ${V=\{MV_1, MV_2, \cdots MV_s\}}$ and ${U \subset V}$ always starting in the first vector, i.e., ${U_n=\{MV_1, MV_2, \cdots MV_n\}}$ with $n\leq s$, the best set of \acs{mv} ($U_n^*)$ is selected based on the optimisation procedure given by (\ref{eq:MVSelectOpt}). Note that any value of $n$ corresponds to a unique subset $U$.
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\begin{equation}\label{eq:MVSelectOpt}
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\newcommand{\argmax}{\operatornamewithlimits{arg\ max}}
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U_n^* = \argmax_{n\leq s} \left\{ \sum_{i=1}^n A_{D}(i) - \alpha \frac{t_C-t_I}{T_{I}} R_U \right\},
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\end{equation}
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where $R_U$ is the amount of bits required to encode the $n$ \acs{mv} of $U$ and $A_{D}(i)$ is the total area of the \acs{pu} whose \acs{mv} are dependently encoded from $\ac{mv}_i$:
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@@ -238,10 +237,10 @@ A_{D}(i) = \sum_{j=1}^{DV_i} ( w_j\times h_j ),\hspace{2mm} i = 1, 2 \cdots s,
|
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\end{equation}
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where $DV_i$ is the number of \acs{mv} dependent from $MV_i$, and $w_j$ and $h_j$ are the width and height of the corresponding \ac{pu}.
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A weighting factor is used to control the amount of bitrate overhead ($R_U$), by increasing the cost of the number of encoded bits based on the duration of the error propagation.
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This duration is given by the ratio between the distance from the current frame ($t_C$) to the \HIGHLIGHT{previous refresh frame} ($t_I$) and the \ac{idr} period ($T_{I}$), combined with the control parameter $\alpha$.
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This duration is given by the ratio between the distance from the current frame ($t_C$) to the previous refresh frame ($t_I$) and the \ac{idr} period ($T_{I}$).
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This weight varies in the range $\left[0;1 \right]$, achieving the lowest value for intra-coded frames and the maximum for the frame located immediately before an intra-coded one.
|
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In the proposed approach, for higher values of $\alpha$, less redundant \acs{mv} are selected.
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||||
As pointed out before, this streaming stage operates on coded data and may be implemented at any point of the network, to increase robustness without fully decoding the stream. Note that the parsing operation represented in Figure~\ref{fig:proposedMethod} does not include decoding of the video frames. Furthermore, only one frame delay is introduced because the \ac{mv} selection process is performed on a frame-by-frame basis.
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||||
Moreover, in the proposed approach, the parameter $\alpha$ is used to globally control the redundancy for the entire stream, resulting in higher values of $\alpha$, less redundant \acs{mv} are selected.
|
||||
As pointed in Section~\ref{Section4:Architecture}, this streaming stage operates on coded data and may be implemented at any point of the network, to increase robustness without fully decoding the stream. Note that the parsing operation represented in Figure~\ref{fig:proposedMethod} does not include decoding of the video frames. Furthermore, only one frame delay is introduced because the \ac{mv} selection process is performed on a frame-by-frame basis.
|
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||||
To maintain compatibility with the \ac{hevc} standard, the redundant motion information is transmitted using the \ac{sei} \ac{nal} units \cite{HEVCHighLevelReferenceDez2012}. Thus, this information can be multiplexed into the coded bitstream without affecting the compressed video.
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||||
The subset of selected \acs{mv} is independently encoded, ensuring that no reference information is needed in the decoder to properly decode the redundant \acs{mv}. In this work arithmetic coding~\cite{ArithmeticCodingJun1994} was used for the side-information, but any other entropy coding method can be used.
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@@ -257,7 +256,7 @@ As references for comparison, three other methods are used: (i) a fixed checkerb
|
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The experimental setup used in this section is the same as the one previously described in Section~\ref{Section3:ErrorResilienceEvaluation}.
|
||||
Two test cases were considered for simulation: (i) in \emph{\LOSTALL} all frames may be equally affected by packet loss, including \ac{idr} frames; (ii) in \emph{\KEEPINTRA} \ac{idr} frames are assumed to be prioritised in the network and delivered error-free, or using some combined approach as recently proposed in \cite{ErrorResilienceHeterogenousNetworks2016}.
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At the decoder-side, \ac{ec} is based on motion compensation from previously decoded frames. The motion field of a missing frame is obtained applying \ac{mv} extrapolation from the two closest neighbours. Afterwards, the final \acs{mv} are selected based on the residual energy of their original prediction~\cite{HEVCMCResidueDec2013}.
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||||
At the decoder-side, \ac{ec} is based on motion compensation from previously decoded frames. The motion field of a missing frame is obtained by applying \ac{mv} extrapolation from the two closest neighbours. Afterwards, the final \acs{mv} are selected based on the residual energy of their original prediction~\cite{HEVCMCResidueDec2013}.
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||||
The evaluation of the encoding-stage is carried out separately from the streaming-stage.
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||||
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@@ -299,7 +298,7 @@ Figure~\ref{fig:RecoveredExample} shows the visual impact of error propagation o
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The proposed method is compared with the method~in~\cite{ROPERefGenJan2010}, as both aim at reducing the error propagation.
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||||
The method~in~\cite{ROPERefGenJan2010} uses filtered frames as reference for predictions, therefore, in case of errors, the effects of mismatched predictions are significantly reduced by the filtering operation (see Sub-Section~\ref{SECTION2:LeakyPred}).
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||||
As shown in Figure~\ref{fig:RecoveredExample}, the proposed method is able to reduce the impact of mismatch decoding in comparison with the method~in~\cite{ROPERefGenJan2010}.
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||||
Although both methods still reveal some predictions mismatch leading to inaccurate reconstructed of the objects contours, the proposed method is able to achieve a smoother reconstruction, which leads to higher quality in terms of \ac{psnr}.
|
||||
Although both methods still reveal some predictions mismatch leading to inaccurate reconstructed of object contours, the proposed method is able to achieve a smoother reconstruction, which leads to higher quality in terms of \ac{psnr}.
|
||||
This is because, in frame \#10, these predictions that depend on the missing frame \#9 are not used throughout the entire frame, thus, approximately half of the predictions are not affected by errors.
|
||||
In contrast, the method~in~\cite{ROPERefGenJan2010} requires several filtering operations to be applied (i.e, different reference frames need to be filtered) in order to reduce the effect of mismatched predictions.
|
||||
Summarising, the reconstructed frames illustrated in Figure~\ref{fig:RecoveredExample} reveal the higher efficiency achieved by distributing the use of the references frames.
|
||||
@@ -310,7 +309,7 @@ Summarising, the reconstructed frames illustrated in Figure~\ref{fig:RecoveredEx
|
||||
\centering
|
||||
% \includegraphics[width=\FIGSIZE]{\ImprovedErrorResilience/Results/PSNRversusFrameKendo}
|
||||
\input{041_ImprovedErrorResilience/Results/PSNRversusFrameKendo.tex}
|
||||
\caption{Error propagation when errors affect Frame \#7 in Kendo sequence with an \ac{idr} period of 32 frames.}
|
||||
\caption{Error propagation when errors affect Frame \#7 in Kendo sequence with an \ac{idr} period of 32 frames for the proposed reference frame selection algorithm in comparison with existing methods.}
|
||||
\label{fig:PSNRvsFrameForRefPicSel}
|
||||
\end{figure}
|
||||
|
||||
@@ -334,9 +333,9 @@ In \cite{ROPERefGenJan2010}, the use of interpolated references in the encoding
|
||||
The proposed method is also able to outperform the \ac{hevc} reference software when an hierarchical coding structure is used (see results for \ac{ra} configuration in Table~\ref{tab:AverageQualityCHKBEXPRandomAccess}), which implicitly limits the error propagation.
|
||||
In comparison to the reference \ac{hevc}, quality improvements up to $1.42$~dB at PLR=$10\%$ are obtained for Kendo sequence.%, while the average gain across all test sequences is 1~dB.
|
||||
|
||||
When comparing results for both test cases, it is noticeable that the proposed method is able to outperform the reference methods for both cases, regardless whether \ac{idr} frame loss is allowed or not (\LOSTALL and \KEEPINTRA, respectively).
|
||||
When comparing the results for both test cases, it is noticeable that the proposed method is able to outperform the reference methods for both cases, regardless whether \ac{idr} frame loss is allowed or not (\LOSTALL and \KEEPINTRA, respectively).
|
||||
As the \ac{plr} increases, the gains of the proposed method decrease in comparison with the case where the \ac{idr} frames are delivered error-free (i.e., case \KEEPINTRA), due to the higher error propagation of the missing \ac{idr} frame. Moreover, the quality gains are lower in the \LOSTALL case when comparing with \KEEPINTRA for the same \ac{plr}, for both the \ac{ld} and \ac{ra}.
|
||||
Taking into account the above results and the spatio-temporal information shown in Table~\ref{TableTestSequences}, one can observe that the performance of the proposed method is lower for sequences with high spatial details and low temporal activity (e.g., BQSquare and Park Scene), which indicates higher effectiveness for video content with higher motion activity.
|
||||
Taking into account the above results and the spatio-temporal information shown in Table~\ref{TableTestSequences}, one can observe that the performance of the proposed method is lower for sequences with high spatial details and low temporal activity (e.g., BQSquare and Park Scene), which indicates higher effectiveness for video content with higher motion activity, as can be seen by the results of Race Horses and Tennis sequences.
|
||||
|
||||
|
||||
\subsubsection{Influence of parameter $\gamma$}
|
||||
@@ -352,7 +351,7 @@ Taking into account the above results and the spatio-temporal information shown
|
||||
\label{fig:QualityVsGamma}
|
||||
\end{figure}
|
||||
|
||||
The influence of the $\gamma$ parameter used in (\ref{eq:KFactor}) on the video quality was also evaluated, in order to check how the cost constrains affect the performance of the proposed method.
|
||||
The influence of $\gamma$ parameter used in (\ref{eq:KFactor}) on the video quality was also evaluated, in order to check how the cost constrains affect the performance of the proposed method.
|
||||
Figure~\ref{fig:QualityVsGamma} shows the average quality gains of the proposed method in comparison to the reference \ac{hevc} (\REF) across all test sequences, for different values of $\gamma$.
|
||||
For the error-free case (PLR=0\%), the results in this figure show that increasing $\gamma$ leads to a decrease in the coding efficiency, especially in the \ac{ld} configuration.
|
||||
The reason is that higher values of $\gamma$ lead to higher values of $W(r_{\phi})$, therefore the reference picture selection cost is severely constrained by the exponential weights.
|
||||
@@ -360,9 +359,8 @@ Since $T_{ID}$ is constant for all frames in the \ac{ld}, higher weights are use
|
||||
In the case where packet loss occurs, higher values of $\gamma$ lead to improved error resilience.
|
||||
Since higher weights are used in the \ac{rd} cost, the encoder is constrained to distribute the use of the reference frames of different \acs{pu}, reducing the error propagation.
|
||||
For very high values (e.g., $\gamma=1000$) the encoder is forced to uniformly select the reference frames, resulting in a fixed checkerboard pattern.
|
||||
Overall, the $\gamma$ parameter is able to control the reference frame selection constrains,
|
||||
%, which are selected by a modified \ac{rd} optimisation, ,
|
||||
thus achieving better trade-off between improved error resilience and loss of coding efficiency.
|
||||
\HIGHLIGHT{Overall, the $\gamma$ parameter is able to control the reference frame selection constrains,
|
||||
thus achieving better trade-off between improved error resilience and loss of coding efficiency. The value of this parameter should be selected accordingly with the conditions regarding particular applications, e.g., loss ratios or network bandwidth.}
|
||||
|
||||
|
||||
\subsubsection{BD-PSNR under error-free conditions}
|
||||
@@ -373,7 +371,7 @@ Table~\ref{tab:bd_error_free} shows the Bjontegaard's Delta PSNR (BD-PSNR) valu
|
||||
\begin{table}[tb]
|
||||
\centering
|
||||
\newcommand{\resultswidth}{1.5cm}
|
||||
\caption{BD-PSNR (dB) in error-free conditions.}
|
||||
\caption{BD-PSNR (dB) in error-free conditions of the proposed reference frame selection algorithm (\PROP) in comparison with existing methods.}
|
||||
\TABLESVSPACE
|
||||
\footnotesize
|
||||
\label{tab:bd_error_free}
|
||||
@@ -393,7 +391,7 @@ Table~\ref{tab:bd_error_free} shows the Bjontegaard's Delta PSNR (BD-PSNR) valu
|
||||
|
||||
The results in Table~\ref{tab:bd_error_free} indicate a small loss of coding efficiency of the proposed method, as well as, the reference methods tested.
|
||||
The fixed checkerboard approach leads to an average quality reduction of $1.22$~dB for the \ac{ld} ($0.32$~dB for \ac{ra}), while the proposed method only loses $0.82$~dB and $0.24$~dB in the \ac{ld} and \ac{ra} configurations, respectively.
|
||||
This results clearly show that dynamically selecting the reference frames is more efficient than using a fixed pattern, confirming the previous results for error-prone conditions.
|
||||
These results clearly show that dynamically selecting the reference frames is more efficient than using a fixed pattern, confirming the previous results for error-prone conditions.
|
||||
Moreover, in comparison to \LONG and the method in~\cite{ROPERefGenJan2010}, the proposed method is still more efficient, because it consistently achieves better results.
|
||||
Comparing, the results for both error-free and error-prone conditions, one may conclude that the proposed method not only achieves higher quality gains, but also reduces the impact on coding efficiency.
|
||||
Overall, the loss in the quality compared to the reference \ac{hevc} is considered acceptable, given the increase in robustness obtained in lossy transmission.
|
||||
@@ -404,7 +402,7 @@ Overall, the loss in the quality compared to the reference \ac{hevc} is consider
|
||||
|
||||
\subsubsection{Complexity overhead}
|
||||
|
||||
The implementation of this method slightly increases the complexity of a standard non-robust encoder. Therefore, the computational complexity has also been considered as a performance metric in this research, which is measured as the encoding time in a controlled hardware platform. The reference used for comparison with other methods is the \ac{hevc} reference software encoding time, running on the same platform with the same coding configuration.
|
||||
The implementation of this method slightly increases the complexity of a standard non-robust encoder. Therefore, the computational complexity has also been considered as a performance metric in this research, which is measured as the average encoding time of five runs, in a controlled hardware platform. The reference used for comparison with other methods is the \ac{hevc} reference software encoding time, running on the same platform with the same coding configuration.
|
||||
The relative complexity increase of the robust encoder using \PROP, \CHKB and the method presented in \cite{ROPERefGenJan2010} is shown in Table~\ref{tab:cpu_time}.
|
||||
|
||||
|
||||
@@ -440,7 +438,7 @@ Different values of $\alpha$ are used to achieve different overhead ratios. All
|
||||
|
||||
\subsubsection{Dynamic MV selection performance}
|
||||
|
||||
Firstly the quality obtained by the proposed method, which dynamically selects the amount of redundant \acs{mv}, is compared to cases where a fixed amount of \acs{mv} per frame are transmitted (i.e., the most important 10\%, 30\% and 50\% $\ac{mv} \in V$). This cased are referred to as "\DYN" and "\FIXED", respectively.
|
||||
Firstly the quality obtained by the proposed method, which dynamically selects the amount of redundant \acs{mv}, is compared to cases where a fixed amount of \acs{mv} per frame are transmitted (i.e., the most important 10\%, 30\% and 50\% $\ac{mv} \in V$). These cases are referred to as "\DYN" and "\FIXED", respectively.
|
||||
|
||||
%In the case where variable redundancy is allowed, \acs{mv} are selected using the proposed dynamic method (Dyn) and a fixed approach (Fixed).
|
||||
Figure~\ref{fig:QualityVsRedundancyMVSelection} illustrates the relation between the quality obtained (\ac{psnr}) and the amount of redundancy used.
|
||||
@@ -460,7 +458,7 @@ Results also show that for both approaches the decoded video quality increases a
|
||||
\label{fig:QualityVsRedundancyMVSelection}
|
||||
\end{figure}
|
||||
|
||||
It is noticeable in Figure~\ref{fig:QualityVsRedundancyMVSelection} that for higher \acs{plr}, the proposed method is able to achieve higher gains.
|
||||
It is noticeable in Figure~\ref{fig:QualityVsRedundancyMVSelection} that for higher \acs{plr}, the proposed method is able to achieve higher gains when compared with the fixed approach.
|
||||
This reveals that when data loss increases it is more important to optimise the amount of \acs{mv} selected per frame, in order to use more \acs{mv} in those frames that have a higher impact on the error propagation.
|
||||
For instance, the proposed method is able to gain up to $1.5$~dB for the BQSquare sequence subject to a PLR=10\%.
|
||||
For lower \acs{plr}, since fewer frames are affected by data losses, the use of a fixed approach achieves similar performance as the proposed method (\DYN).
|
||||
@@ -470,7 +468,7 @@ For lower \acs{plr}, since fewer frames are affected by data losses, the use of
|
||||
|
||||
\begin{table}[!t]
|
||||
\centering
|
||||
\caption{Average quality using redundant \ac{mv} selected by the proposed approach.}
|
||||
\caption{Average PSNR (dB) using redundant \ac{mv} selected by the proposed approach.}
|
||||
\TABLESVSPACE
|
||||
\label{tab:AverageQualityRedundancies}
|
||||
\TABLESVSPACE
|
||||
@@ -483,11 +481,11 @@ This study was also conducted for the two different cases, with and without \ac{
|
||||
It can be confirmed from the results of Table~\ref{tab:AverageQualityRedundancies} that, for each sequence, as $\alpha$ decreases more redundant \acs{mv} are used, leading to higher overhead rations. This results in higher objective video quality for all tested sequences.
|
||||
These results also confirm that redundant \acs{mv} are able to minimise the error propagation, which is accomplished at the cost of an acceptable increase in the bitrate, since only a sub-set of \acs{mv} is transmitted.
|
||||
The use of redundant \acs{mv} yields better video quality for \ac{plr} as low as $1\%$, achieving higher quality gains when \ac{idr} frames are not lost (\KEEPINTRA). This is because of the higher quality degradation when \ac{idr} frames are lost, which is not due to mismatched \ac{mv} predictions, and thus cannot be reduced by transmitting extra \acs{mv} as side-information.
|
||||
The use the proposed method also achieves higher quality for sequences with higher motion activity that make use of inter-prediction more often, i.e., BQSquare and Traffic sequences (see Inter ratio in Table~\ref{TableTestSequences}), leading to a maximum gain of $3.93$~dB for \LOSTALL ($7.82$~dB for \KEEPINTRA).
|
||||
The use of the proposed method also achieves higher quality for sequences with higher motion activity that make use of inter-prediction more often, i.e., BQSquare and Traffic sequences (see Inter ratio in Table~\ref{TableTestSequences}), leading to a maximum gain of $3.93$~dB for \LOSTALL ($7.82$~dB for \KEEPINTRA).
|
||||
|
||||
In summary, the proposed method is able to select the most relevant motion \ac{mv} information and when combined with a reference picture selection scheme the problem of mismatch \ac{mv} predictions is mitigated.
|
||||
Moreover, the dynamic approach devised to select the best \acs{mv} also contributes to increase the overall performance.
|
||||
Thus, using the proposed two-stage approach to recover temporal predictions consistently improves the error robustness of \ac{hevc}.
|
||||
Thus, using the proposed two-stage approach to recovering temporal predictions consistently improves the error robustness of \ac{hevc}.
|
||||
|
||||
|
||||
|
||||
@@ -497,7 +495,7 @@ Thus, using the proposed two-stage approach to recover temporal predictions cons
|
||||
In this chapter a two-stage approach was proposed to increase the error robustness of \ac{hevc} streaming and reduce the error propagation in case of packet loss.
|
||||
A constrained coding approach was devised to select reference frames and dynamically distribute temporal dependencies.
|
||||
This is jointly used with a controlled amount of side information in coded streams, comprising a small set of the most relevant \acs{mv}, in order to minimise \ac{mv} mismatch at the decoder in the presence of frame loss. The use of \acs{mv} as side information was firstly proposed and evaluated in the publication~(\ref{Conf:MotionVectorRedundancies}) and the reference frame selection was further discussed in the publication~(\ref{Conf:Chkb}). Subsequently, a wider analysis was carried out and published in (\ref{Journal:TwoStage}).
|
||||
As can be concluded from the results, this method contributes to reduce the temporal dependencies with consistent quality gains for different \ac{plr} and coding configurations, only incurring in a small drop of coding efficiency.
|
||||
As can be concluded from the results, this method contributes to reduce the temporal dependencies with consistent quality gains for different \acs{plr} and coding configurations, only incurring a small drop of coding efficiency.
|
||||
Overall, the proposed approach is an effective method to cope with video transmission over error-prone networks.
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
@@ -1,44 +1,51 @@
|
||||
set terminal postscript eps monochrome font 'TimesNewRoman,16'
|
||||
|
||||
set size {0.75,0.75}
|
||||
set xlabel "Sequence" font ",18" offset character 0, 0.4, 0
|
||||
set xtics center font ",16" # offset character 0, 0, 0
|
||||
set ylabel "Reference Usage Ratio (%)" font ",18" offset character 0.9, 0, 0
|
||||
set ytics font ",16"
|
||||
set terminal postscript eps enhanced font 'TimesNewRoman,16'
|
||||
# set terminal pdfcairo mono font 'TimesNewRoman,14'
|
||||
|
||||
|
||||
load '../../carreira_phd_thesis_gnuplot_template.cfg'
|
||||
unset grid
|
||||
unset xlabel
|
||||
set ylabel 'Reference Usage (\%)'
|
||||
|
||||
set style line 1 lc 4 lt 4 lw 3 pt 10 ps 1
|
||||
#set style line 2 lc rgbcolor "#006400" lt 2 lw 3 pt 3 ps 1
|
||||
set style line 2 lc 2 lt 2 lw 3 pt 3 ps 1
|
||||
set style line 3 lc 3 lt 4 lw 3 pt 4 ps 1
|
||||
set style line 4 lc 1 lt 1 lw 3 pt 2 ps 1
|
||||
set style line 5 lc 7 lt 3 lw 3 pt 6 ps 1
|
||||
set style line 6 lc 8 lt 6 lw 3 pt 8 ps 1
|
||||
set style line 7 lc 2 lt 7 lw 3 pt 8 ps 1
|
||||
set style line 8 lc 3 lt 8 lw 3 pt 9 ps 1
|
||||
set style line 9 lc 4 lt 9 lw 3 pt 10 ps 1
|
||||
set style histogram clustered
|
||||
#set style histogram rowstacked
|
||||
|
||||
|
||||
set style histogram clustered gap 1.5
|
||||
set grid y
|
||||
set style histogram gap 1.5
|
||||
set style data histograms
|
||||
set style fill solid
|
||||
set boxwidth 1
|
||||
set boxwidth 0.8
|
||||
set key off
|
||||
set xtics rotate by 30 offset 0,-1
|
||||
set xtics right rotate by 20 offset .5,-.1
|
||||
set bmargin 5
|
||||
|
||||
set yrange[0.4:1.0]
|
||||
set ytics 0.4,.1,1.0
|
||||
set output 'plot_AverageRefPicUsage_lowdelay_P_2Ref_main_16_rate_2M000bps_0.eps'
|
||||
plot '-' using 3:xtic(2) linecolor 9
|
||||
0 "Kendo" 0.839689
|
||||
1 "Book Arrival" 0.873156
|
||||
set style line 200 lc rgb '#696969' lt 1 lw 2 pi -1 ps 1.0
|
||||
set style line 201 lc rgb '#4d4d4d' lt 1 lw 2 pi -1 ps 1.0
|
||||
set yrange[50:100]
|
||||
set ytics 40,10,100
|
||||
set output 'plot.eps'
|
||||
plot '-' using ($3*100):xtic(2) ls 200, '-' using (100-$3*100) ls 201
|
||||
0 "Book Arrival" 0.873156
|
||||
1 "BQSquare" 0.5816
|
||||
2 "Basketball Drill" 0.836044
|
||||
3 "Race Horses" 0.885911
|
||||
4 "BQSquare" 0.5816
|
||||
5 "Tennis" 0.865467
|
||||
6 "Park Scene" 0.849022
|
||||
3 "Kendo" 0.839689
|
||||
4 "Park Scene" 0.849022
|
||||
5 "Race Horses" 0.885911
|
||||
6 "Tennis" 0.865467
|
||||
e
|
||||
|
||||
0 "Book Arrival" 0.873156
|
||||
1 "BQSquare" 0.5816
|
||||
2 "Basketball Drill" 0.836044
|
||||
3 "Kendo" 0.839689
|
||||
4 "Park Scene" 0.849022
|
||||
5 "Race Horses" 0.885911
|
||||
6 "Tennis" 0.865467
|
||||
e
|
||||
# plot '-' using ($3*100):xtic(2) ls 201, '' using (100-$3*100) ls 201
|
||||
# 0 "\\TitleFont Book Arrival" 0.873156
|
||||
# 1 "\\TitleFont BQSquare" 0.5816
|
||||
# 2 "\\TitleFont Basketball Drill" 0.836044
|
||||
# 3 "\\TitleFont Kendo" 0.839689
|
||||
# 4 "\\TitleFont Park Scene" 0.849022
|
||||
# 5 "\\TitleFont Race Horses" 0.885911
|
||||
# 6 "\\TitleFont Tennis" 0.865467
|
||||
# e
|
||||
|
||||
@@ -13,20 +13,30 @@ set grid y
|
||||
set style histogram gap 1.5
|
||||
set style data histograms
|
||||
set style fill solid
|
||||
set boxwidth 1
|
||||
set key off
|
||||
set boxwidth 0.8
|
||||
set key top right samplen 1.3 width -1.2
|
||||
set xtics right rotate by 20 offset .5,-.1
|
||||
set bmargin 5
|
||||
set style line 20 lc rgb '#696969' lt 1 lw 2 pi -1 ps 1.0
|
||||
set yrange[50:100]
|
||||
set ytics 40,10,100
|
||||
plot '-' using ($3*100):xtic(2) ls 20
|
||||
0 "\\TitleFont Book Arrival" 0.873156
|
||||
1 "\\TitleFont BQSquare" 0.5816
|
||||
set style line 200 lc rgb '#b5b5b5' lt 1 lw 2 pi -1 ps 1.0
|
||||
set style line 201 lc rgb '#444444' lt 1 lw 2 pi -1 ps 1.0
|
||||
set yrange[0:100]
|
||||
set ytics 0,10,100
|
||||
plot '-' using ($3*100):xtic(2) title '\KeyFont $r_1$' ls 200, '-' using ($3*100) title '\KeyFont $r_2$' ls 201
|
||||
0 "\\TitleFont Book Arrival" 0.873156
|
||||
1 "\\TitleFont BQSquare" 0.5816
|
||||
2 "\\TitleFont Basketball Drill" 0.836044
|
||||
3 "\\TitleFont Kendo" 0.839689
|
||||
4 "\\TitleFont Park Scene" 0.849022
|
||||
5 "\\TitleFont Race Horses" 0.885911
|
||||
6 "\\TitleFont Tennis" 0.865467
|
||||
3 "\\TitleFont Kendo" 0.839689
|
||||
4 "\\TitleFont Park Scene" 0.849022
|
||||
5 "\\TitleFont Race Horses" 0.885911
|
||||
6 "\\TitleFont Tennis" 0.865467
|
||||
e
|
||||
0 "\\TitleFont Book Arrival" 0.12684
|
||||
1 "\\TitleFont BQSquare" 0.41840
|
||||
2 "\\TitleFont Basketball Drill" 0.16396
|
||||
3 "\\TitleFont Kendo" 0.16031
|
||||
4 "\\TitleFont Park Scene" 0.15098
|
||||
5 "\\TitleFont Race Horses" 0.11409
|
||||
6 "\\TitleFont Tennis" 0.13453
|
||||
e
|
||||
|
||||
\end{gnuplot}
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
\newcommand\nlines{4}
|
||||
\newcommand{\resultswidth}{1.3cm}
|
||||
\centering
|
||||
\caption{Average quality (PSNR): Low-delay configuration (LD).}
|
||||
\caption{Average PSNR (dB) under random loss events obtained with the proposed reference frame selection and three reference methods using the Low-delay configuration (LD).}
|
||||
\TABLESVSPACE
|
||||
\small
|
||||
\footnotesize
|
||||
@@ -18,72 +18,72 @@
|
||||
& \REF & 32.25 & 24.43 & 36.52 & 33.70 & 31.70 & 28.51 \\
|
||||
& \AVG & -1.05 & +0.50 & -1.95 & +0.12 & +1.32 & +2.90 \\
|
||||
& \CHKB & -0.39 & +0.60 & -0.09 & +0.69 & +0.97 & +1.34 \\
|
||||
& \EXP & +0.08 & +0.86 & +0.45 & +1.26 & +1.54 & +1.78 \\
|
||||
& \PROP & +0.08 & +0.86 & +0.45 & +1.26 & +1.54 & +1.78 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Book Arrival }}
|
||||
& \REF & 34.47 & 26.95 & 38.53 & 35.82 & 33.63 & 30.67 \\
|
||||
& \AVG & -1.84 & -0.72 & -1.76 & -0.64 & -0.05 & -0.04 \\
|
||||
& \CHKB & -0.08 & +0.81 & +0.49 & +1.33 & +1.66 & +2.15 \\
|
||||
& \EXP & -0.37 & +0.56 & +0.35 & +1.17 & +1.41 & +1.55 \\
|
||||
& \PROP & -0.37 & +0.56 & +0.35 & +1.17 & +1.41 & +1.55 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Cactus }}
|
||||
& \REF & 34.37 & 31.94 & 37.04 & 35.67 & 34.51 & 32.65 \\
|
||||
& \AVG & -0.13 & +0.20 & +0.31 & +0.46 & +0.35 & +0.83 \\
|
||||
& \CHKB & -0.34 & -0.12 & +0.02 & +0.37 & +0.71 & +1.00 \\
|
||||
& \EXP & -0.23 & +0.04 & +0.06 & +0.21 & +0.43 & +0.54 \\
|
||||
& \PROP & -0.23 & +0.04 & +0.06 & +0.21 & +0.43 & +0.54 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Bosphorus }}
|
||||
& \REF & 31.79 & 24.00 & 42.19 & 41.31 & 40.56 & 38.74 \\
|
||||
& \AVG & -0.09 & -1.04 & -0.34 & -0.15 & +0.09 & +0.67 \\
|
||||
& \CHKB & +0.16 & -0.49 & -0.38 & -0.13 & +0.13 & +0.72 \\
|
||||
& \EXP & +0.23 & -0.31 & -0.47 & -0.18 & +0.07 & +0.69 \\
|
||||
& \PROP & +0.23 & -0.31 & -0.47 & -0.18 & +0.07 & +0.69 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{BQSquare }}
|
||||
& \REF & 32.47 & 25.11 & 38.20 & 36.08 & 34.57 & 31.93 \\
|
||||
& \AVG & -4.42 & -3.79 & -4.42 & -3.79 & -3.73 & -3.74 \\
|
||||
& \CHKB & -0.38 & +0.63 & -0.12 & +0.29 & +0.44 & +0.67 \\
|
||||
& \EXP & +0.74 & +1.37 & -0.14 & +0.66 & +0.88 & +1.35 \\
|
||||
& \PROP & +0.74 & +1.37 & -0.14 & +0.66 & +0.88 & +1.35 \\
|
||||
\hline
|
||||
%\multirow{\nlines}{*}{\vbox{Jockey }}
|
||||
%& \REF & 33.52 & 26.57 & 37.91 & 33.23 & 30.75 & 27.53 \\
|
||||
%& \AVG & +1.29 & -0.03 & +2.10 & +3.21 & +2.84 & +1.00 \\
|
||||
%& \CHKB & +2.16 & +0.19 & +1.47 & +2.03 & +2.20 & +2.05 \\
|
||||
%& \EXP & +2.17 & +0.23 & +1.68 & +2.41 & +2.30 & +2.16 \\
|
||||
%& \PROP & +2.17 & +0.23 & +1.68 & +2.41 & +2.30 & +2.16 \\
|
||||
%\hline
|
||||
\multirow{\nlines}{*}{\vbox{Kendo }}
|
||||
& \REF & 38.49 & 30.89 & 40.45 & 36.79 & 33.80 & 30.35 \\
|
||||
& \AVG & -1.82 & +0.07 & -1.82 & +0.10 & +1.34 & +1.30 \\
|
||||
& \CHKB & +0.09 & +1.16 & +0.24 & +1.38 & +2.27 & +2.15 \\
|
||||
& \EXP & +0.51 & +1.33 & +0.19 & +1.34 & +2.26 & +2.26 \\
|
||||
& \PROP & +0.51 & +1.33 & +0.19 & +1.34 & +2.26 & +2.26 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Park Scene }}
|
||||
& \REF & 29.81 & 25.29 & 36.06 & 34.83 & 33.64 & 31.63 \\
|
||||
& \AVG & -2.29 & -1.63 & -3.49 & -3.40 & -3.34 & -3.45 \\
|
||||
& \CHKB & -0.05 & +0.10 & -0.69 & -0.20 & +0.22 & +0.71 \\
|
||||
& \EXP & -0.08 & -0.04 & -0.50 & -0.05 & +0.29 & +0.86 \\
|
||||
& \PROP & -0.08 & -0.04 & -0.50 & -0.05 & +0.29 & +0.86 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{People on Street }}
|
||||
& \REF & 26.07 & 21.36 & 32.42 & 29.61 & 27.85 & 25.00 \\
|
||||
& \AVG & +0.26 & +0.82 & -0.66 & +0.42 & +0.83 & +1.26 \\
|
||||
& \CHKB & +1.05 & +0.43 & +0.43 & +0.95 & +1.13 & +1.38 \\
|
||||
& \EXP & +0.94 & +0.34 & +0.58 & +1.02 & +1.17 & +1.42 \\
|
||||
& \PROP & +0.94 & +0.34 & +0.58 & +1.02 & +1.17 & +1.42 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Race Horses }}
|
||||
& \REF & 29.34 & 23.88 & 33.00 & 29.41 & 27.49 & 24.55 \\
|
||||
& \AVG & -0.10 & +1.25 & +0.60 & +2.71 & +3.31 & +3.88 \\
|
||||
& \CHKB & +0.69 & +0.64 & +0.46 & +1.35 & +1.47 & +1.64 \\
|
||||
& \EXP & +1.11 & +0.84 & +0.79 & +1.80 & +1.94 & +2.11 \\
|
||||
& \PROP & +1.11 & +0.84 & +0.79 & +1.80 & +1.94 & +2.11 \\
|
||||
\hline
|
||||
\multirow{\nlines}{*}{\vbox{Tennis }}
|
||||
& \REF & 32.50 & 25.69 & 36.50 & 31.90 & 29.67 & 26.15 \\
|
||||
& \AVG & -1.04 & +0.12 & -1.74 & -0.72 & -0.08 & +1.71 \\
|
||||
& \CHKB & +0.48 & +0.45 & +0.69 & +1.19 & +0.81 & +1.17 \\
|
||||
& \EXP & +1.15 & +1.17 & +1.20 & +2.03 & +1.95 & +2.12 \\
|
||||
& \PROP & +1.15 & +1.17 & +1.20 & +2.03 & +1.95 & +2.12 \\
|
||||
% \hline
|
||||
% \multirow{3}{*}{\vbox{\textbf{Average} }}
|
||||
% & \textbf{\AVG} & \textbf{-0.95} & \textbf{-0.34} & \textbf{-1.16} & \textbf{-0.15} & \textbf{+0.25} & \textbf{+0.59} \\
|
||||
% & \textbf{\CHKB} & \textbf{+0.30} & \textbf{+0.38} & \textbf{+0.17} & \textbf{+0.78} & \textbf{+1.04} & \textbf{+1.33} \\
|
||||
% & \textbf{\EXP} & \textbf{+0.50} & \textbf{+0.53} & \textbf{+0.33} & \textbf{+1.00} & \textbf{+1.22} & \textbf{+1.47} \\
|
||||
% & \textbf{\PROP} & \textbf{+0.50} & \textbf{+0.53} & \textbf{+0.33} & \textbf{+1.00} & \textbf{+1.22} & \textbf{+1.47} \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
}
|
||||
@@ -94,7 +94,7 @@
|
||||
\newcommand{\resultswidth}{1.3cm}
|
||||
\newcommand\nlines{4}
|
||||
\centering
|
||||
\caption{Average quality (PSNR): Random-access configuration (RA).}
|
||||
\caption{Average PSNR (dB) under random loss events obtained with the proposed reference frame selection and three reference methods using the Random-access configuration (RA).}
|
||||
\TABLESVSPACE
|
||||
\small
|
||||
\footnotesize
|
||||
@@ -140,7 +140,7 @@
|
||||
%& \REF & 33.86 & 26.52 & 36.80 & 31.82 & 29.27 & 26.21 \\
|
||||
%& \LONG & +1.22 & +0.45 & +0.70 & +0.50 & +0.25 & +0.05 \\
|
||||
%& \CHKB & +1.00 & +0.79 & +0.74 & +1.08 & +1.12 & +0.96 \\
|
||||
%& \EXP & +1.08 & +0.86 & +0.85 & +1.30 & +1.16 & +1.13 \\
|
||||
%& \PROP & +1.08 & +0.86 & +0.85 & +1.30 & +1.16 & +1.13 \\
|
||||
%\hline
|
||||
\multirow{\nlines}{*}{\vbox{Kendo }}
|
||||
& \REF & 36.81 & 28.11 & 39.78 & 34.69 & 30.73 & 26.62 \\
|
||||
@@ -176,12 +176,12 @@
|
||||
% & \REF & 28.60 & 23.47 & 37.29 & 34.52 & 32.54 & 29.60 \\
|
||||
% & \LONG & -0.09 & -0.31 & -0.37 & -0.32 & -0.34 & -0.24 \\
|
||||
% & \CHKB & -0.05 & -0.07 & +0.16 & +0.54 & +0.63 & +0.96 \\
|
||||
% & \EXP & -0.09 & +0.06 & +0.41 & +0.61 & +0.76 & +0.95 \\
|
||||
% & \PROP & -0.09 & +0.06 & +0.41 & +0.61 & +0.76 & +0.95 \\
|
||||
% \hline
|
||||
% \multirow{3}{*}{\vbox{\textbf{Average} }}
|
||||
% & \textbf{\LONG} & \textbf{+0.23} & \textbf{+0.15} & \textbf{-0.10} & \textbf{+0.22} & \textbf{+0.28} & \textbf{+0.16} \\
|
||||
% & \textbf{\CHKB} & \textbf{+0.30} & \textbf{+0.40} & \textbf{+0.29} & \textbf{+0.53} & \textbf{+0.69} & \textbf{+0.78} \\
|
||||
% & \textbf{\EXP} & \textbf{+0.53} & \textbf{+0.64} & \textbf{+0.43} & \textbf{+0.75} & \textbf{+0.87} & \textbf{+0.94} \\
|
||||
% & \textbf{\PROP} & \textbf{+0.53} & \textbf{+0.64} & \textbf{+0.43} & \textbf{+0.75} & \textbf{+0.87} & \textbf{+0.94} \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user