23,707 research outputs found
BLENDED LEARNING STRATEGIES: OPPORTUNITIES AND LIMITATIONS OF USING YOUTUBE VIDEOS TO SUPPORT READY RELEVANT LEARNING
In this thesis, we develop a business case analysis of the use of YouTube with the Navy’s Ready Relevant Learning. We assess current literature regarding the effectiveness of YouTube videos for enhancing adult learning and job performance. We then identify the requirements for the Navy to implement YouTube learning and discuss the advantages and limitations associated with using YouTube for learning. Additionally, we conduct a qualitative analysis of data obtained from interviews with students and instructors at a Navy vocational training site on the use of digital learning strategies. We place the main findings from the interviews in the context of the current literature to validate that YouTube can be an effective tool to enhance learning. We conclude that YouTube is a viable tool for vocational training and we recommend that the Navy conduct a pilot program to identify implementation needs to scale effectively the use of YouTube as a training tool.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Lieutenant Commander, United States NavyLieutenant, United States NavyApproved for public release. Distribution is unlimited
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
Optimal grouping-of-pictures in IoT video streams
We study a dynamic video encoder that detects scene changes and tunes the synthesis of Groups-of-Pictures accordingly. Such dynamic encoding can be applied to infrastructures with restricted resources, like IoT facilities where multimedia streams are of use. In such facilities the scarcity of resources (energy, bandwidth, etc.) is a dominant solution design factor. In the domain of video capturing/transmission content-driven approaches should be adopted to improve efficiency while maintaining quality at acceptable levels. We propose a time-optimized decision making model that yields different sizes of groups-of-pictures (frames) to meet the previously discussed objectives i.e., transmit video sequences in acceptable quality with rational use of the wireless resources. Our quantitative findings show that the propose scheme performs quite efficiently while dispatching video sequences with different characteristics
Efficient HEVC-based video adaptation using transcoding
In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints.
These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency.
This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications
A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks
139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements
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