nils-herrmann/clean_scirepeval huggingface dataset:
Optimizations and Economics of Crowdsourced Mobile Streaming

Mobile video traffic accounts for more than half of the global mobile data traffic nowadays, and the ratio is expected to further increase in the near future. However, providing high quality of experience for video streaming in mobile networks is challenging due to the heterogeneous and varying wireless channel conditions. To meet the increasing demand of high-quality mobile video streaming services, researchers have proposed several cooperative video streaming models that enable mobile users to download video contents cooperatively. The key idea is to pool network edge resources so as to either alleviate the load on the video servers and the cellular network, or alleviate the impact of channel variations and improve resource utilization. In this article, we review four types of cooperative video streaming models that pool various network resources effectively in different application scenarios. Then we focus on the crowdsourced mobile streaming model, which aims to pool users' download capacities in order to alleviate the impact of channel variations and achieve efficient utilization of network resources. We introduce the corresponding optimization issue of efficient resource allocation and the economic issue of user cooperation. We also outline future challenges and open issues in cooperative video streaming models.
A new scheme for multiple source video streaming

Video streaming is one of the most important applications used in the best-effort Internet. This paper presents a new scheme for multiple source video streaming in which the traditional fine granular scalable coding was rebuilt into a multiple sub-streams based transmission model. A PSNR based stream rate allocation algorithm was then developed based on the transmission model. In tests, the algorithm performance is about 1dB higher than that of a uniform rate allocation algorithm. Therefore, this scheme can overcome bottlenecks along a single link and smooth jitter to achieve high quality, stable video.
Guyot: a hybrid learning- and model-based RTT predictive approach

Knowing the Round-Trip Time (RTT) between a client and a server is important for an online interactive multi-media service to provide satisfactory quality of user experience. Due to the intrinsic dynamics of the topology and routing strategies in the Internet, it is however challenging to predict the RTT accurately from limited information, e.g., only the IP pair of the client and server. To address this challenge, we propose Guyot, a hybrid learning- and model-based approach to predict RTT, which requires significantly smaller amount of data to be collected than traditional approaches, while achieving a similar prediction accuracy. Our design is based on a large-scale measurement study from a content provider's perspective. Based on an information gain analysis, we design a hybrid RTT prediction approach involving two types of predictions: (1) Learning-based prediction: We train a decision tree to predict RTT between IP pairs with large geographic distance, requiring only a small set of features to be collected. (2) Model-based prediction: We use a model-based framework to predict RTT between IP pairs with small distance, providing an accurate RTT prediction over time. By strategically dividing RTT prediction tasks to these two types according to the distance of the inferred geo-locations of the IPs, our prediction approach can scale with satisfactory accuracy. Our experiments further confirm the superiority of our design.
Strategies of buffering schedule in P2P VoD streaming

As compared to live peer-to-peer (P2P) streaming, modern P2P video-on-demand (VoD) systems have brought much larger volumes of videos and more interactive controls to the Internet users. As the increase of bitrate of the videos and the full VCR controls of P2P VoD, the behavior “buffering” motivates us to design different schedule and service strategies for peers, to improve the playback performance, and the alleviation of the dedicated streaming server, by making best use of the bandwidth and cache capacities of these buffering peers. In our design, peers strategically decide which segments in the video to download first, and which requests to serve first. We conduct extended simulations to evaluate the performance of the strategies, and the results show our design outperforms the conventional sequential scheme, with respect to improving the playback quality and reducing the server load.
High-resolution 3D reconstruction for complex color scenes with structured light

High-resolution 3D reconstruction has become increasingly important due to its wide applications such as 3D printing, entertainment, engineering etc. In this paper, a high-resolution 3D reconstruction system based on structured light is proposed. Our proposed system contains a consuming projector and a digital camera. Unlike the traditional structured light which directly projects patterns into scenes, we project extended patterns generated from a 2D mother pattern and analysis the luminance changes of pixels of captured images to restore the mother pattern. With the help of extended patterns, the proposed system can not only deal with complex scenes robustly, but also deal with color scenes. And since the positions are matched based on 2D perfect map, external parameters of the projector and digital camera are not required during the evaluation. Experimental results show that the proposed system can generates high-resolution surfaces.
Awareness-driven media stream transmitting framework of virtual tele-conferencing

In this paper, we propose. an awareness-driven media stream transmitting framework of virtual tele-conferencing. First we introduce a space model of awareness and Interaction, which define the awareness and interaction between objects and determine the media streams transmitting at run time. Based on the space model of awareness, we design a multicast middleware above the socket layer; participants of a multicast session are connected via a virtual multicast tree that consists of unicast connections between end hosts where the cost of each link is the awareness degree between sender and receiver. According to different awareness degree, different type of data such as status, live audio and video will be transmitted to different objects composing dynamic multicast group. This framework supports flexible dynamic multicast, which effectively reduces the total bandwidth and has high scalability for system extension.
Highlight detection in soccer video using web-casting text

Highlight detection is a challenge task in soccer video analysis. Using Web-casting text as external knowledge is proved to be a short cut to achieve both efficiency and effectiveness. Based on the previous framework using Web-casting text, we have improved the processes of video time detection and highlight boundary detection. Our method can detect the transparent time bar and can achieve acceptable precision in highlight boundary detection though the Web text time is not accurate at all. This progress can make the framework more robust in practice.
Mid-Level Descriptors Extraction of Soccer Video with Domain Knowledge

In this paper, we propose a method for two typical mid-level descriptors extraction of soccer video: view type and playfield position. The output reveals high-level structure of the soccer video. After the robust playfield segmentation, we use the grass-area-ratio and the boundary approximate lines between playfield and non-playfield to classify each frame into three kinds of view types according to a series of rules. We then classify frames captured by the main camera into fifteen kinds of positions by two steps: first, we classify the frames into five kinds according to the orientation of boundary lines; secondly, we use lines detection to further determine the specific playfield position. Experiments on three hours soccer videos show promising results.
A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events

In this paper we propose a technique to detect anomalies in individual and interactive event sequences. We categorize anomalies into two classes: abnormal event, and abnormal context, and model them in the Sequential Monte Carlo framework which is extended by Markov Random Field for tracking interactive events. Firstly, we propose a novel pixel-wise event representation method to construct feature images, in which each blob corresponds to a visual event. Then we transform the original blob-level features into subspaces to model probabilistic appearance manifolds for each event-class. With the probability of an observation associated with each event-class (or state) derived from probabilistic manifolds, and state transitional probability, the prior and posterior state distributions can be estimated. We demonstrate in experiments that the approach can reliably detect such anomalies with low false alarm rates.
Towards Network-Failure-Tolerant Web Content Delivery: A Path-Aware Peer-Assisted Approach

Popularly used to distribute a variety of multimedia content items in today's Internet, HTTP-based web content delivery still suffers from various content delivery failures, including server failures , network failures  and routing failures . Hindered by the expensive deployment cost, the conventional CDN can not deploy as many edge servers as possible to successfully deliver content items to all users under these delivery failures. In this paper, we propose a joint CDN and peer-assisted web content delivery framework to address the delivery failure problem. Different from conventional peer- assisted approaches for web content delivery, which mainly focus on alleviating the CDN servers' bandwidth load, we study how to use a browser-based peer-assisted scheme, namely WebRTC, to resolve content delivery failures. To this end, we carry out large-scale measurement studies on how users access and view webpages. Our measurement results demonstrate the challenges (e.g., peers stay on a webpage extremely short) that can not be directly solved by conventional P2P strategies, and some important webpage viewing patterns (e.g., predictability of users' webpage viewing time). Due to these unique characteristics, WebRTC peers open up new possibilities for helping the web content delivery, coming with the problem of how to utilize the dynamic resources efficiently. We formulate the peer selection that is the critical strategy in our framework, as an optimization problem, and design a heuristic algorithm based on the mea- surement insights to solve it. Our simulation experiments driven by the traces from Tencent QZone, one of the most popular social service platforms in China, demonstrate the effectiveness of our design: compared with non-peer-assisted strategy and random peer selection strategy, our design significantly improves the successful relay ratio of web content items under network failures, e.g., our design improves the content download ratio up to 60% even when users located in a particular region (e.g., city) where none can connect to the regional CDN server.
Crowdsourced mobility prediction based on spatio-temporal contexts

Accurate mobility prediction is becoming increasingly important in human behavior research, mainly due to many location-based applications such as mobile social networks and mobile advertisements. In this work, we propose a new crowd-sourced human mobility prediction model for public regions. We first analyze human trajectories collected through a cluster of densely deployed Wi-Fi access points (AP) in a shopping mall, and then characterize the close relationship between the human mobility patterns and the spatio-temporal contexts. Based on the distinct features of human trajectories in different types of public regions, we further propose a Markov-based crowdsourced mobility prediction method utilizing spatio-temporal contexts. We evaluate the performance of the proposed method using real traces, and show that our method is 28% more accurate in predicting human location transitions and incurs 14% smaller error in stay time prediction than the baseline methods.
Overlay collaboration towards reduced bandwidth costs in multi-view streaming

Delivering high-quality multiview video (MV) through Internet is very challenging due to its excessive consumption on server bandwidth resources. Existing solutions encode video contents independently for each view and deliver them separately in isolated view channels, without leveraging the features of MV and taking advantage of multiview video coding(MVC). To minimize the server bandwidth costs, we introduce a novel overlay collaboration framework that unifies all view channels to cooperate in delivering MV: I pictures in MVC are shared among them instead of requesting from server respectively; Surplus resources of hotspot view channels are effectively utilized to help channels with insufficient resources, both of which contribute to remarkable reduction in server bandwidth costs. Simulation experiments show that our method achieves more than 40% reduced bandwidth costs on server while maintaining scalability and resilience to user dynamics.
Robust inter-scale non-blind image motion deblurring

Kernel estimate errors and image noise are major causes of visual artifacts in image motion deblurring. We propose an inter-scale non-blind image motion deblurring approach that significantly reduces those artifacts. We use Gaussian Scale Mixture Field of Experts (GSM FOE) model as image prior. The inter-scale smoothness constraint is adopted to suppress the ringing artifacts. In each scale, image details are recovered by the residual deconvolution and the cross bilateral filter (CBF). We further propose a std-controlled CBF to denoise the result. The experimental results aremuch better than those of previous methods.
Performance bound analysis for crowdsourced mobile video streaming

Adaptive bitrate (ABR) streaming enables video users to adapt the playing bitrate to the real-time network conditions to achieve the desirable quality of experience (QoE). In this work, we propose a novel crowdsourced streaming framework for multi-user ABR video streaming over wireless networks. This framework enables the nearby mobile video users to crowdsource their radio links and resources for cooperative video streaming. We focus on analyzing the social welfare performance bound of the proposed crowdsourced streaming system. Directly solving this bound is challenging due to the asynchronous operations of users. To this end, we introduce a virtual time-slotted system with the synchronized operations, and formulate the associated social welfare optimization problem as a linear programming. We show that the optimal social welfare performance of the virtual system provides effective upper-bound and lower-bound for the optimal performance (bound) of the original asynchronous system, hence characterizes the feasible performance region of the proposed crowdsourced streaming system. The performance bounds derived in this work can serve as a benchmark for the future online algorithm design and incentive mechanism design.
A metric of stereoscopic image retargeting quality assessment

Many previous works on the stereoscopic image retargeting are proposed. In this paper, we present the first study on objective assessment for stereoscopic image retargeting quality (SIRQ). Firstly, we build a dataset using four kinds of stereoscopic image retargeting methods and conduct a subjective experiment to evaluate the retargeted images according to ITU's recommendations. Then we introduce a novel measurement for SIRQ by fusing picture quality and stereo perception quality. We derive an objective metric and validate it by a user study. Finally, we analyse the metric's performance and discuss the influence factors. It shows that many works are needed to be researched further in the future.
iGridMedia: Providing Delay-Guaranteed Peer-to-Peer Live Streaming Service on Internet

Almost all existing peer-to-peer (P2P) streaming systems can only provide non-interactive service on Internet. In this paper, we focus on providing delay-guaranteed service to support delay-tolerant interactive applications (such as online auction, person interview, video sharing & commenting, etc.) using P2P streaming technology. In an interactive channel, all or part of the participants can interact with the presenter or publisher at the source. Unlike existing P2P streaming service, there is different level of delay and synchronization requirement in the interactive applications, meanwhile, the participant number in each channel is relatively small but the concurrent channel number is large. With such challenges, how much server bandwidth can be saved in P2P streaming is an interesting problem. We propose a very practical protocol iGridMedia and fully implement it. The basic tradeoff between consumed server bandwidth and the required delay is carefully studied. Simulation and real-world experiment show that our system consumes only about 4 times streaming rate with 5 sec guaranteed delay even if the peer churn rate is high. So far as we know, our work represents the state-of- the-art approach that using P2P technology to support emergent interactive live applications on Internet with delay-guaranteed requirement.
Pedestrian detection using improved histogram of oriented gradients

Histogram of oriented gradients (HOG) has been proved to be an effective way of solving pedestrian detection in real scenes, but great computation amount makes it far from practical application. So we propose a new detection system based on improved HOG features with faster detecting speed. While calculating HOG, the pixels irrelevant to object contour shape are neglected. Then we decrease dimension number of the feature vector by 80%, making the SVM classification model much more simplified. Although the accuracy only drops down no more than 5%, our improved method performs over 5 times faster than the previous one. After entirely scanning a image, we use mean shift clustering algorithm to merge multiple positive responses belonging to same one positive case. Experiments show our method's performance on both test dataset and in real scene images.
Natural interaction synthesizing in virtual teleconferencing

The virtual teleconferencing system provides a collaborative virtual environment for applications such as collaborative seminar. The challenge of the natural interaction is for the participants to be aware of the virtual environment. A video avatar is a computer-synthesized three-dimension image created using live video. We study the factors contributing to the participant's natural interaction and introduce a video avatar based participant model to implement the natural, accurate and convenient interaction among participants. Our virtual teleconferencing prototype provides a seamless virtual collaboration work environment including eye contacts and gaze awareness. Our experiments on a three parties collaborative seminar show that natural interaction synthesizing can significantly improve the communication and that natural multi-model human computer interaction is necessary in collaboration.
Scalable Video Stream Transmissions Strategy with Subflow Divisions over Multistandard Integrated Networks

In this paper, we propose a scalable video transmission strategy over multistandard integrated networks based on H.264/AVC SVC extension streaming. In this proposed transmission strategy, SVC streams are divided into several subflows and different flows have different temporal resolution and delay constraints. For broadcasting network with hierarchical modulation scheme in the multistandard integrated networks, such as DVB-H and DVB-T, the proposed subflows division strategy can enable a smart use of the available bandwidth. For wireless network transmission standards with the resource reserved QoS assurance in the multistandard integrated networks, such as IEEE 802.11e, transmission of suitable subflows subset over the channels can decrease the delay and admit more users without much compromise in the video quality for the already admitted users.
Path-aware peer-assisted web content delivery against network failures

Popularly used to distribute a variety of multimedia contents in today's Internet, HTTP-based web content delivery still suffers from failures occurring both in the network and at the servers. Limited by the deployment of edge servers, it is hard for a CDN to successfully deliver contents to all users under these network failures. Different from conventional peer-assisted approaches for web content delivery, which mainly focus on alleviating the CDN servers' bandwidth load, we study how to use a browser-based peer-assisted scheme to resolve content delivery failures. Based on large-scale measurement studies on how users access and view webpages, we observe the challenges (e.g., peers stay on a webpage extremely short) that cannot be directly solved by conventional P2P strategies, and some important webpage viewing patterns. Due to these unique characteristics, WebRTC peers give us a novel way to deliver web content, coming with the problem of how to utilize the dynamic resources efficiently. We formulate the peer selection as an optimization problem, and design a heuristic algorithm based on the measurement insights to strategically select the most appropriate peer. Our prototype implementation on Tencent QZone and simulation experiments further demonstrate the effectiveness of our design: compared with non-peer-assisted strategy and random peer selection strategy, our design significantly improves the successful relay ratio of web contents under network failures, e.g., our design achieves a successful content download ratio of 60% even when users located in a particular region are not able to connect to the CDN servers.
Energy-Efficient Cross-Layer Rate Allocation for Multi-Source Streaming over Ad Hoc Networks

In this paper, we study the case that a number of cameras capture and transfer video sequences to a receiver over ad hoc networks, just as surveillance scenarios. In order to prolong system lifetime and improve the video qualities, we propose a dynamic rate allocation scheme using cross-layer techniques. The allocation is performed at the receiver of the video streams and the decision is based on a rate-power model for network transmission and a rate-distortion model of video. Experiment result shows that the proposed scheme promotes the average PSNR by 0.8-3.0 dB without shortening the system lifetime, or prolongs system lifetime by 12%-50% without cutting down the overall PSNR