YouTube as a repository : the creative practice of students as producers of Open Educational Resources

In this paper we present an alternative view of Open Educational Resources (OERs). Rather than focusing on open media resources produced by expert practitioners for use by peers and learners, we examine the practice of learners as active agents, producing open media resources using the devices in their pockets: their mobile phones. In this study, students are the producers and operate simultaneously as legitimate members of the YouTube community and producers of educational content for future cohorts. Taking an Action Research approach we investigated how student’s engagement with open media resources related to their creativity. Using Kleiman’s framework of fives conceptual themes which emerged from academics experiences of creativity (constraint, process, product, transformation, fulfillment), we found that these themes revealed the opportunities designed into the assessed task and provided a useful lens with which to view students’ authentic creative experiences. Students’ experience of creativity mapped on to Kleiman’s framework, and was affected by assessment. Dimensions of openness changed across platforms, although the impact of authenticity and publication on creativity was evident, and the production of open media resources that have a dual function as OERs has clear benefits in terms of knowledge sharing and community participation.The transformational impacts for students were evident in the short term but would merit a longitudinal study. A series of conclusions are drawn to inform future practice and research

Automatic mashup generation of multiple-camera videos

The amount of user generated video content is growing enormously with the increase in availability and affordability of technologies for video capturing (e.g. camcorders, mobile-phones), storing (e.g. magnetic and optical devices, online storage services), and sharing (e.g. broadband internet, social networks). It has become a common sight at social occasions like parties, concerts, weddings, vacations that many people are shooting videos at approximately the same time. Such concurrent recordings provide multiple views of the same event. In professional video production, the use of multiple cameras is very common. In order to compose an interesting video to watch, audio and video segments from different recordings are mixed into a single video stream. However, in case of non-professional recordings, mixing different camera recordings is not common as the process is considered very time consuming and requires expertise to do. In this thesis, we research on how to automatically combine multiple-camera recordings in a single video stream, called as a mashup. Since non-professional recordings, in general, are characterized by low signal quality and lack of artistic appeal, our objective is to use mashups to enrich the viewing experience of such recordings. In order to define a target application and collect requirements for a mashup, we conducted a study by involving experts on video editing and general camera users by means of interviews and focus groups. Based on the study results, we decided to work on the domain of concert video. We listed the requirements for concert video mashups such as image quality, diversity, and synchronization. According to the requirements, we proposed a solution approach for mashup generation and introduced a formal model consisting of pre-processing, mashupcomposition and post-processing steps. This thesis describes the pre-processing and mashup-composition steps, which result in the automatic generation of a mashup satisfying a set of the elicited requirements. At the pre-processing step, we synchronized multiple-camera recordings to be represented in a common time-line. We proposed and developed synchronization methods based on detecting and matching audio and video features extracted from the recorded content. We developed three realizations of the approach using different features: still-camera flashes in video, audio-fingerprints and audio-onsets. The realizations are independent of the frame rate of the recordings, the number of cameras and provide the synchronization offset accuracy at frame level. Based on their performance in a common data-set, audio-fingerprint and audio-onset were found as the most suitable to apply in generating mashups of concert videos. In the mashup-composition step, we proposed an optimization based solution to compose a mashup from the synchronized recordings. The solution is based on maximizing an objective function containing a number of parameters, which represent the requirements that influence the mashup quality. The function is subjected to a number of constraints, which represent the requirements that must be fulfilled in a mashup. Different audio-visual feature extraction and analysis techniques were employed to measure the degree of fulfillment of the requirements represented in the objective function. We developed an algorithm, first-fit, to compose a mashup satisfying the constraints and maximizing the objective function. Finally, to validate our solution approach, we evaluated the mashups generated by the first-fit algorithm with the ones generated by two other methods. In the first method, naive, a mashup was generated by satisfying only the requirements given as constraints and in the second method, manual, a mashup was created by a professional. In the objective evaluation, first-fit mashups scored higher than both the manual and naive mashups. To assess the end-user satisfaction, we also conducted a user study where we measured user preferences on the mashups generated by the three methods on different aspects of mashup quality. In all the aspects, the naive mashup scored significantly low, while the manual and first-fit mashups scored similarly. We can conclude that the perceived quality of a mashup generated by the naive method is lower than first-fit and manual while the perceived quality of the mashups generated by first-fit and manual methods are similar

Quality Assessment of Mobile Phone Video Stabilization

Smartphone cameras are used more than ever for photography and videography. This has driven mobile phone manufacturers to develop and enhance cameras in their mobile phones. While mobile phone cameras have evolved a lot, many aspects of the mobile phone camera still have room for improvement. One is video stabilization which aims to remove unpleasant motion and artifacts from video. Many video stabilization methods for mobile phones exist. However, there is no standard video stabilization quality assessment (VSQA) framework for comparing the performance of the video stabilization methods. Huawei wanted to improve the video stabilization quality of their mobile phones by investigating video stabilization quality assessment. As a part of that endeavor, this work studies existing VSQA frameworks found in the literature and incorporates some of their ideas into a VSQA framework established in this work. The new VSQA framework consists of a repeatable laboratory environment and objective sharpness and motion metrics. To test the VSQA framework, videos were captured on multiple mobile phones in the laboratory environment. These videos were first subjectively evaluated to find issues that are noticeable by humans. Then the videos were objectively evaluated with the objective sharpness and motion metrics. The results show that the proposed VSQA framework can be used for comparing and ranking mobile devices. The VSQA framework successfully identifies the strengths and weaknesses of each tested device's video stabilization quality.Älypuhelimien kameroita käytetään nykyään valokuvaukseen enemmän kuin koskaan. Tämä on saanut älypuhelimien valmistajia kehittämään heidän puhelimiensa kameroita. Vaikka paljon edistystä on tapahtunut, niin moni älypuhelimen kameran osa-alueista kaipaa vielä kehitystä. Yksi heikoista osa-alueista on videostabilointi. Videostabiloinnin tarkoitus on poistaa videosta epämiellyttävä liike. Monia ratkaisuja löytyy, mutta mitään standardoitua tapaa vertailla eri stabilointi ratkaisuja ei ole. Huawei haluaa parantaa tuotteidensa videostabiloinnin laatua. Saavuttaakseen tämän tavoitteen, tässä työssä tehdään katsaus kirjallisuudesta löytyviä videostabiloinnin laadun mittausmenetelmiä ja jalostetaan näistä ideoita, joiden avulla kehitetään oma videonstabiloinnin laadun mittausmenetelmä. Menetelmä koostuu toistettavasta laboratorioympäristöstä, jossa voi kuvata heiluvia videoita eri älypuhelimilla. Näitä videoita vertaillaan objektiivisesti mittaamalla videoista terävyyttä ja liikkeen miellyttävyyttä. Työn videostabiloinnin laadun mittausmenetelmää testattiin kuvaamalla toistettavassa laboratorioympäristössä usealla älypuhelimella videoita, joissa on simuloitua käden tärinää. Ensin kuvattuja videoita arvioitiin ja vertailtiin subjektiivisesti, jotta niistä löytyisi ongelmat, joita videostabilointi ei ole onnistunut korjaamaan. Tämän jälkeen videoita arvioitiin objektiivisilla terävyys- ja liikemittareilla. Tulokset osoittavat, että työssä esitetty videostabiloinnin laadun mittausmenetelmää voidaan käyttää eri älypuhelimien videostabilointimenetelmien vertailuun. Työn mittausmenetelmä onnistui havaitsemaan eri video stabilointimenetelmien vahvuudet ja heikkoudet

Balancing automation and user control in a home video editing system

The context of this PhD project is the area of multimedia content management, in particular interaction with home videos. Nowadays, more and more home videos are produced, shared and edited. Home videos are captured by amateur users, mainly to document their lives. People frequently edit home videos, to select and keep the best parts of their visual memories and to add to them a touch of personal creativity. However, most users find the current products for video editing timeconsuming and sometimes too technical and difficult. One reason of the large amount of time required for editing is the slow accessibility caused by the temporal dimension of videos: a video needs to be played back in order to be watched or edited. Another reason of the limitation of current video editing tools is that they are modelled too much on professional video editing systems, including technical details like frame-by-frame browsing. This thesis aims at making home video editing more efficient and easier for the non-technical, amateur user. To accomplish this goal, an approach was taken characterized by two main guidelines. We designed a semi-automatic tool, and we adopted a user-centered approach. To gain insights on user behaviours and needs related to home video editing, we designed an Internet-based survey, which was answered by 180 home video users. The results of the survey revealed the facts that video editing is done frequently and is seen as a very time-consuming activity. We also found that users with low experience with PCs often consider video editing programs too complex. Although nearly all commercial editing tools are designed for a PC, many of our respondents said to be interested in doing video editing on a TV. We created a novel concept, Edit While Watching, designed to be user-friendly. It requires only a TV set and a remote control, instead of a PC. The video that the user inputs to the system is automatically analyzed and structured in small video segments. The editing operations happen on the basis of these video segments: the user is not aware anymore of the single video frames. After the input video has been analyzed and structured, a first edited version is automatically prepared. Successively, Edit While Watching allows the user to modify and enrich the automatically edited video while watching it. When the user is satisfied, the video can be saved to a DVD or to another storage medium. We performed two iterations of system implementation and use testing to refine our concept. After the first iteration, we discovered that two requirements were insufficiently addressed: to have an overview of the video and to precisely control which video content to keep or to discard. The second version of EditWhileWatching was designed to address these points. It allows the user to visualize the video at three levels of detail: the different chapters (or scenes) of the video, the shots inside one chapter, and the timeline representation of a single shot. Also, the second version allows the users to edit the video at different levels of automation. For example, the user can choose an event in the video (e.g. a child playing with a toy) and just ask the system to automatically include more content related to it. Alternatively, if the user wants more control, he or she can precisely select which content to add to the video. We evaluated the second version of our tool by inviting nine users to edit their own home videos with it. The users judged Edit While Watching as an easy to use and fast application. However, some of them missed the possibility of enriching the video with transitions, music, text and pictures. Our test showed that the requirements of overview on the video and control in the selection of the edited material are better addressed than in the first version. Moreover, the participants were able to select which video portions to keep or to discard in a time close to the playback time of the video. The second version of Edit While Watching exploits different levels of automation. In some editing functions the user only gives an indication about editing a clip, and the system automatically decides the start and end points of the part of the video to be cut. However, there are also editing functions in which the user has complete control on the start and end points of a cut. We wanted to investigate how to balance automation and user control to optimize the perceived ease of use, the perceived control, the objective editing efficiency and the mental effort. To this aim, we implemented three types of editing functions, each type representing a different balance between automation and user control. To compare these three levels, we invited 25 users to perform pre-defined tasks with the three function types. The results showed that the type of functions with the highest level of automation performed worse than the two other types, according to both subjective and objective measurements. The other two types of functions were equally liked. However, some users clearly preferred the functions that allowed faster editing while others preferred the functions that gave full control and a more complete overview. In conclusion, on the basis of this research some design guidelines can be offered for building an easy and efficient video editing application. Such application should automatically structure the video, eliminate the detail about single frames, support a scalable video overview, implement a rich set of editing functionalities, and should be preferably TV-based

Automatic extraction of constraints in manipulation tasks for autonomy and interaction

Tasks routinely executed by humans involve sequences of actions performed with high dexterity and coordination. Fully specifying these actions such that a robot could replicate the task is often difficult. Furthermore the uncertainties introduced by the use of different tools or changing configurations demand the specification to be generic, while enhancing the important task aspects, i.e. the constraints. Therefore the first challenge of this thesis is inferring these constraints from repeated demonstrations. In addition humans explaining a task to another person rely on the person's ability to apprehend missing or implicit information. Therefore observations contain user-specific cues, alongside knowledge on performing the task. Thus our second challenge is correlating the task constraints with the user behavior for improving the robot's performance. We address these challenges using a Programming by Demonstration framework. In the first part of the thesis we describe an approach for decomposing demonstrations into actions and extracting task-space constraints as continuous features that apply throughout each action. The constraints consist of: (1) the reference frame for performing manipulation, (2) the variables of interest relative to this frame, allowing a decomposition in force and position control, and (3) a stiffness gain modulating the contribution of force and position. We then extend this approach to asymmetrical bimanual tasks by extracting features that enable arm coordination: the master--slave role that enables precedence, and the motion--motion or force--motion coordination that facilitates the physical interaction through an object. The set of constraints and the time-independent encoding of each action form a task prototype, used to execute the task. In the second part of the thesis we focus on discovering additional features implicit in the demonstrations with respect to two aspects of the teaching interactions: (1) characterizing the user performance and (2) improving the user behavior. For the first goal we assess the skill of the user and implicitly the quality of the demonstrations by using objective task--specific metrics, related directly to the constraints. We further analyze ways of making the user aware of the robot's state during teaching by providing task--related feedback. The feedback has a direct influence on both the teaching efficiency and the user's perception of the interaction. We evaluated our approaches on robotic experiments that encompass daily activities using two 7 degrees of freedom Kuka LWR robotic arms, and a 53 degrees of freedom iCub humanoid robot

IMPACT OF VIDEO RESOLUTION CHANGES ON QoE FOR ADAPTIVE VIDEO STREAMING

HTTP adaptive streaming (HAS) has become the de-facto standard for video streaming to ensure continuous multimedia service delivery under irregularly changing network conditions. Many studies already investigated the detrimental impact of various playback characteristics on the Quality of Experience of end users, such as initial loading, stalling or quality variations. However, dedicated studies tackling the impact of resolution adaptation are still missing. This paper presents the results of an immersive audiovisual quality assessment test comprising 84 test sequences from four different video content types, emulated with an HAS adaptation mechanism. We employed a novel approach based on systematic creation of adaptivity conditions which were assigned to source sequences based on their spatio-temporal characteristics. Our experiment investigates the resolution switch effect with respect to the degradations in MOS for certain adaptation patterns. We further demonstrate that the content type and resolution change patterns have a significant impact on the perception of resolution changes. These findings will help develop better QoE models and adaptation mechanisms for HAS systems in the future