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13
src/system-bookmarks/bookmark.tex
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@ -16,9 +16,9 @@ A screenshot of this interface is displayed in Figure~\ref{sb:desktop}.
\copied{}
Mobile interactions are more complex because the user does not have neither keyboard nor mouse to interact with.
However, there are some other sensors on most mobile devices that can help interaction.
The most useful sensor for 3D interaction on mobile devices is definitely the gyroscope.
One useful sensor for 3D interaction on mobile devices is definitely the gyroscope.
We use the gyroscope to enable a user to rotate his device to rotate the virtual camera.
We also add the possibility to rotate the camera by drag and dropping the scene.
We also add the possibility to rotate the camera by using touch controls.
This way, the user is not forced to perform a real-world half-turn to be able to look behind or to keep its device pointing to the sky (which can quickly become tiring) to look up.
These interactions, however, do not allow the user to move the camera: he can rotate it but not translate it.
For this reason, we display a small joystick on the bottom left corner of the screen that mimics the first person video games interactions and allow the user translating the camera:
@ -48,11 +48,11 @@ For these reasons, we changed the display to a vertical bar with a 2D sprite of
This 2D sprite is always facing the camera to prevent it from being invisible when the camera would be on the side of it.
Screenshots of user interfaces with bookmarks are available in Figures~\ref{sb:desktop} and~\ref{sb:mobile}.
The size of the sprite changes when time goes by following a sine function to help the user distinguish what is part of the scene and what is extra widgets.
The size of the sprite changes over time following a sine function to help the user distinguish what is part of the scene and what is extra widgets.
Since our scene is static, a user knows that a changing object is not part of the scene, but part of the UI\@.
The other bookmark parameters remain unchanged since Chapter~\ref{bi}: in order to avoid users to lose context, clicking on a bookmark triggers an automatic, smooth, camera displacement that ends up at the bookmark.
We also display a thumbnail of the bookmark's viewpoint when the mouse hovers over a bookmark.
We also display a thumbnail of the bookmark's viewpoint when the mouse hovers a bookmark.
Such thumbnail is displayed in Figure~\ref{sb:desktop}.
Note that since on mobile, there is no mouse and thus no pointer, thumbnails are never downloaded nor displayed.
@ -160,6 +160,7 @@ These renderings allow us to know what geometry segment and what texture corresp
Figure~\ref{sb:precomputation} shows how this precomputation improves the quality of rendering.
Each curve represents the PSNR one can obtain by downloading a certain amount of data, and they show that, for the same amount of data downloaded, the optimized order reaches a higher PSNR than the greedy order, which means that its utility metric is more accurate.
This curve is averaged on all the 9 bookmarks of the scene: we decided the locations of the bookmarks and each bookmark faces an interesting object in the scene.
\begin{figure}[th]
\centering
@ -190,8 +191,8 @@ Each curve represents the PSNR one can obtain by downloading a certain amount of
\copied{}
We now present how to include bookmarks information in the Media Presentation Description (MPD) file
Bookmarks are fully defined by a position, a direction, and the additional content needed to properly render and use a bookmark in a system consists in three images: a thumbnail of the point of view at the bookmark, along with the JSON file giving the optimal segment order for this viewpoint.
We now present how to include bookmarks information in the Media Presentation Description (MPD) file.
Bookmarks are fully defined by a position, a direction, and the additional content needed to properly render and use a bookmark in a system consists in two files: a thumbnail of the point of view at the bookmark, along with the JSON file giving the optimal segment order for this viewpoint.
For this reason, for each bookmark, we create a separate adaptation set in the MPD\@.
The bookmarked viewpoint information is stored as a supplemental property.
Bookmarks adaptation set only contain one representation, composed of two segments: the thumbnail used as a preview for the desktop interface and the JSON file.

14
src/system-bookmarks/introduction.tex
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@ -2,16 +2,16 @@
\section{Introduction}
In Chapter~\ref{bi}, we described how it is possible to modify a user interface to ease user nagivation in a 3D scene, and how the system can exploit it.
In Chapter~\ref{d3}, we presented a streaming system that does not take the interface or the user interaction into account at all.
Hence, it seems natural to us to try to bring back the user interaction into DASH-3D.
In order to do so, we have chosen two angles of attack:
In Chapter~\ref{bi}, we described how it is possible to modify a user interface to ease user navigation in a 3D scene, and how the system can benefit from it.
In Chapter~\ref{d3}, we presented a streaming system that takes neither the interface nor the user interaction into account.
Hence, it is natural study how the user interaction can impact performances of DASH-3D.
In order to do so, followed these two steps:
\begin{itemize}
\item we design an interface allowing to navigate in a 3D scene for both desktop and mobile devices;
\item we design an interface allowing to navigate in a 3D scene on both desktop and mobile devices;
\item we improve and adapt the bookmarks described in Chapter~\ref{bi} to the context of DASH-3D and to mobile interaction.
\end{itemize}
In Section~\ref{sb:interaction}, we present the different choices we made for the interfaces, and we describe the new mobile interface.
In Section~\ref{sb:bookmarks}, we describe how we embed the bookmarks into our DASH framework, and how we precompute data in order to improve the quality of experience of the user.
In Section~\ref{sb:evaluation}, we describe the user study we conducted, the data we collected and we analyse this data.
In Section~\ref{sb:bookmarks}, we describe how we embed the bookmarks into our DASH framework, and how we precompute data in order to improve the user quality of experience.
In Section~\ref{sb:evaluation}, we describe the user study we conducted, the data we collected and we analyse the results.

2
src/system-bookmarks/main.tex
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@ -1,4 +1,4 @@
\chapter{Mobile interaction and system bookmarks\label{sb}}
\chapter{Bookmarks for DASH-3D on mobile devices\label{sb}}
\minitoc{}
\newpage

17
src/system-bookmarks/user-study.tex
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@ -11,16 +11,16 @@ Since we already conduct a user study on desktop devices, we decide to conduct t
In this user study, we use two models.
\begin{itemize}
\item For the tutorial, we use a first model from a video game, representing a small scene, to maintain a good framerate and to prevent users from getting lost in the scene.
\item For all the other parts of the experiment, we kept using the model used in the previous chapter.
\item For all the other parts of the experiment, we used an extended version of the model used in the previous chapter.
\end{itemize}
\subsubsection{Experiment}
The experiment consists in 4 phases: a tutorial, a comparison between interfaces with and without bookmarks, a comparison between two streaming policies, and a final navigation where the user is looking for objects in the scene.
The experiment consists in 4 phases: a tutorial, a comparison between interfaces with and without bookmarks, a comparison between two streaming policies, and a final navigation during which the user is looking for objects in the scene.
\paragraph{Tutorial}
The experiment starts with a tutorial, so the users can get accustomed to our interface.
The experiment starts with a tutorial, to get the users accustomed to our interface.
This tutorial shows the different types of interactions available and explains how to use them.
It then presents bookmarks to the users.
@ -39,7 +39,8 @@ This part of the experiment also consists in two 1 minute long sessions that use
One of those experiment has the default greedy policy described in~\ref{d3:dash-adaptation}, and the other one has the enhanced policy for bookmarks.
The order of those two sessions is randomized to avoid biases.
Since we know that the difference between our streaming policies are subtle, we designed a task a little more complex in order to highlight the differences so that the user can see it.
% Since we know that the difference between our streaming policies is subtle, we designed a task a little more complex in order to highlight the differences so that the user can see it.
Since the behaviours of our streaming policy only differ when the user clicks a bookmark, we designed a task where the users have to perform a guided tour of the scene, where each bookmark is a step of the tour.
The user starts in the scene, and one of the bookmarks is blinking.
The user has to click the bookmark, and wait a little when he arrives at the destination.
Once some data has been downloaded, and the user is satisfied with the data downloaded, they can look for the next blinking bookmarks.
@ -50,8 +51,9 @@ The questionnaire also has a text field for users to explain their answer if the
The last part of the experiment is a free navigation.
Diamonds are hidden in the scene, and are invisible until the user is close enough.
The users have to find the diamonds, and they can navigate by using the controls and the bookmarks.
The loading policy is the default greedy policy for half of the users, and the enhanced policy for bookmarks for the other half.
The users have to find the diamonds, and they can navigate by using indifferently the controls and the bookmarks.
The loading policy is the default greedy policy for half of the users, and the enhanced policy for bookmarks for the other half, and this order has been randomized.
With this part of the experiment, we hope to see differences in terms of PSNR for the two policies, when users are not forced to click on bookmarks.
\subsubsection{Apparatus\todo{lol, i like this title but im not sure}}
@ -62,7 +64,8 @@ There is no artificial bandwidth limitation due to the fact that the bandwidth i
\subsection{Results}
18 users participated in this user-study.
18 users participated in this user-study, 15 males and 3 females, average age is 20.7 and standard deviation is 0.53.
We only proposed this user study to relatively young people to ensure they are used to mobile devices.
\subsubsection{Qualitative results --- Interaction}