nils-herrmann/clean_scirepeval huggingface dataset:
CCL-SVC: Optimizing user experience of broadcasting video on computation capability limited handheld devices

In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.
Topic mining on web-shared videos

Internet videos have grown exponentially with the help from video sharing Websites. Automatic topic mining is therefore increasingly important for organizing and navigating such large video databases. Most of current solutions of topic detection and mining were done on news videos and cannot be directly applied on Web videos, because of their limited and noisy semantic information. In this paper, we will try to address this problem and propose an automatic topic mining framework on Web videos. We develop an iterative weight-updated co-clustering scheme to filter "noisy" tags and mine the "hot" topics. We then propose a visual-based clustering approach to further group the videos with similar content, and rank the visual-similar groups by their similarity to the topic center. Experiments on a large Web video database demonstrate the superior performance of our weight-updated co-clustering to both of the traditional co-clustering and k-means. The experiments also demonstrate significant improvement of users' experience by our visual-based clustering and ranking.
A multi-view video coding approach using Layered Depth Image

Multi-view video introduces more forms of interactivity and can potentially be used for a variety of applications, such as free-view video (FVV) and three-dimensional video (3DTV). However, the huge volume video data and the synthesis of virtual view limit the practicality of multi-view video. In this paper, we propose an approach to multi-view video coding, in which the mutli-view video sequences are converted to layered depth images (LDIs) to represent scenes, and compressed layer by layer. To achieve better compression efficiency, we restructure layered images according depth data in LDIs. Experimental results show that our approach is practicable and efficient to meet the compression efficiency and flexible interactivity.
How to Promote TV Series? Evaluating Actors' Behavior on Social Media

While social networks have become primary promotion platforms for TV series, it's crucial to provide reliable measurements of promotion effectiveness for actors, which can guide them to select better promotion strategies when they post microblogs. In this article, influence indexes are proposed to measure the influence of microblogs, and some measurements on actors' microblogs also indicate and reveal some useful patterns in their promotion behaviors. Then a propensity score matching method is applied to these data to identify effective promotion strategies at two promotion periods. In experiments, the proposed model is shown to be significant by t-test evaluation and the model is demonstrated to be adequately specified by balance diagnostics. With this application of microblog data, the causal effects between promotion strategies and promotion results can be assessed, and appropriate promotion strategies can be chosen to achieve maximum publicity.
Joint Request Balancing and Content Aggregation in Crowdsourced CDN

Recent years have witnessed a new content delivery paradigm named crowdsourced CDN, in which devices deployed at edge network can prefetch contents and provide content delivery service. Crowdsourced CDN offers high-quality experience to end-users by reducing their content access latency and alleviates the load of network backbone by making use of network and storage resources at millions of edge devices. In such paradigm, redirecting content requests to proper devices is critical for user experience. The uniqueness of request redirection in such crowdsourced CDN lies that: on one hand, the bandwidth capacity of the crowdsourced CDN devices is limit, hence devices located at a crowded place can be easily overwhelmed when serving nearby user requests; on the other hand, contents requested in one device can be significantly different from another one, making request redirection strategies used in conventional CDNs which only aim to balance request loads ineffective. In this paper, we explore request redirection strategies that take both workload balance of devices and content requested by users into consideration. Our contributions are as follows. First, we conduct measurement studies, coving 1.8M users watching 0.4M videos, to understand request patterns in crowdsourced CDN. We observe that the loads of nearby devices can be very different and the contents requested at nearby devices can also be significantly different. These observations lead to our design for request balancing at nearby devices. Second, we formulate the request redirection problem by taking both the content access latency and the content replication cost into consideration, and propose a request balancing and content aggregation solution. Finally, we evaluate the performance of our design using trace-driven simulations, and observe our scheme outperforms the traditional strategy in terms of many metrics, e.g., we observe a content access latency reduction by 50% over traditional mechanisms such as the Nearest/Random request routing scheme.
Insights into local stereo matching: Evaluation of disparity refinement approaches

Local stereo matching as a main category of stereo methods has been studied for decades. Recently, there appeared some well-performed local stereo methods and these state-of-the art methods show comparable performance in terms of quality and better performance in terms of speed with global methods on the Middlebury datasets. However, previous surveys of stereo matching do not consider disparity refinement step, which plays a significant role in improving depth quality for local methods. Thus, in this paper, we focus on the evaluation of the existing disparity refinement approaches and provide some insights into local stereo matching.
Group Recommendation Using External Followee for Social TV

Group recommendation plays a significant role in Social TV systems, where online friends form into temporary groups to enjoy watching video together and interact with each other. Online microblogging systems introduce the "following" relationship that reflects the common interests between users in a group and external representative followees outside the group. Traditional group recommendation only considers internal group members' preferences and their relationship. In our study, we measure the external followees' impact on group interest and establish group preference model based on external experts' guidance for group recommendation. In addition, we take advantage of the current watching video to improve context-aware recommendations. Experimental results show that our solution works much better in situations of high group dynamic and inactive group members than traditional approaches.
Toward Wi-Fi AP-Assisted Content Prefetching for an On-Demand TV Series: A Learning-Based Approach

The emergence of smart Wi-Fi access points (AP), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users’ quality of experience (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching which predicts and accurately fetches contents ahead of users’ requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users’ AP connection and TV series watching patterns using real traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov decision process. The objective is to strike a balance between the increased prefetching and storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first we investigate the performance lower bound by using a random algorithm and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.
Fast and robust detection of near-duplicates in web video database

Web video databases tend to contain tremendous near-duplicates with the explosive growth of online videos. How to detect and eliminate these near-duplicates has become an essential problem for Web video storage and indexing. In this paper, we propose a hierarchical approach to solve this problem efficiently and effectively. For an incoming video, firstly we compare global features to exclude videos with large differences. Then pairwise comparison of key-frame level features with more computation cost is performed. For pairwise comparison, we adopt the maximum matching (MM) framework on bipartite graph. Instead of using standard well-known MM algorithm, a simple approximate algorithm of MM and a graph cut algorithm are applied for a higher speed. Experimental results over two databases summing up to more than 20,000 videos demonstrate our algorithmpsilas excellent performance in both speed and accuracy.
Enhancing Internet-Scale Video Service Deployment Using Microblog-Based Prediction

Online microblogging has been very popular in today's Internet, where users follow other people they are interested in and exchange information between themselves. Among these exchanges, video links are a representative type on a microblogging site. The impact is fundamental-not only are viewers in a video service directly coming from the microblog sharing and recommendation, but also are the users in the microblogging site representing a promising sample to all the viewers. It is intriguing to study a proactive service deployment for such videos, using the propagation patterns of microblogs. Based on extensive traces from Youku and Tencent Weibo, a popular video sharing site and a favored microblogging system, we explore how video propagation patterns in the microblogging system are correlated with video popularity on the video sharing site. Using influential factors summarized from the measurement studies, we further design a neural network-based learning framework to predict the number of potential viewers and their geographic distribution. We then design proactive video deployment algorithms based on the prediction framework, which not only determines the upload capacities of servers in different regions, but also strategically replicates videos to these regions to serve users. Our PlanetLab-based experiments verify the effectiveness of our design.
Analyzing Image Deblurring Through Three Paradigms

To recover a sharp version from a blurred image is a long-standing inverse problem. In this paper, we analyze the research on this topic both theoretically and experimentally through three paradigms: 1) the deterministic filter; 2) Bayesian estimation; and 3) the conjunctive deblurring algorithm (CODA), which performs the deterministic filter and Bayesian estimation in a conjunctive manner. We point out the weaknesses of the deterministic filter and unify the limitation latent in two kinds of Bayesian estimators. We further explain why the CODA is able to handle quite large blurs beyond Bayesian estimation. Finally, we propose a novel method to overcome several unreported limitations of the CODA. Although extensive experiments demonstrate that our method outperforms state-of-the-art methods with a large margin, some common problems of image deblurring still remain unsolved and should attract further research efforts.
Delay-guaranteed interactive multiview video streaming

Multiview video is well known to involve interactions with audience and offer better view experience than conventional single-view video. The interactive nature of multiview video has made the service very delay-sensitive and thus imposes great challenges for providing multiview streaming service in Internet. Currently, almost none of existing works have addressed the delay issue in multiview streaming. To ensure the quality of service and offer preferable view experience for users, we propose a novel streaming framework to provide delay-guaranteed service for interactive multiview video. The basic tradeoff between consumed server bandwidth and the required delay is carefully studied. We leverage the features of MVC to keep the server bandwidth costs low while guarantee the delay constraint. In addition, we introduce a neighbor-assisted view switching scheme: peer's neighbors are involved in its switching process and their upload bandwidth resources are effectively utilized to reduce the bandwidth costs on server. Simulation results show that our proposed framework meet the delay-guaranteed requirement with restrained server bandwidth costs while maintaining scalability and resilience to users dynamics.
Fast Arc Detection Algorithm for Play Field Registration in Soccer Video Mining

This paper presents an LSF-based framework for detecting arcs in broadcast soccer video. The successful identification of the arcs will evidently facilitate the soccer video analysis. The existing methods are not available for all playfield arcs including both the middle field circle and penalty box arcs. A new algorithm is proposed in the paper improved from LSF, called ALSF (advanced least square fitting), which can be used to detect arcs even though they are only 1/4 of eclipses (the same as penalty box arcs). With the improvement, we proposed a new framework to detect the arcs in broadcast soccer video. The proposed method first removes the points of straight lines. Then, all points of each connective area are transformed into a new reference frame to use LSF to get the equations of arcs. Experiments on more than 3 hours broadcast soccer video show the proposed method is effective with above 98% precision and 70% recall.
Beyond 1 Million Nodes: A Crowdsourced Video Content Delivery Network

To deliver video traffic with guaranteed quality of experience, providers have been deploying content delivery networks (CDNs) closer to users. CDN providers are also starting to use the storage and network resources of edge network devices for content delivery--that is, crowdsourced CDN. This article envisions a crowdsourced CDN and proposes a set of practical strategies to guide the implementation of this new paradigm. The authors perform a large-scale measurement to explore the popularity of geocontent. They then design solutions for the system's operation, including regional content popularity prediction, region partition, and collaborative content replication. In addition, they propose an auction mechanism to cope with resource competition among multiple content providers, and they demonstrate the performance gain of the design using data-driven simulation.
Background subtraction in dynamic scenes with adaptive spatial fusing

Background subtraction in highly dynamic scenes has been a critical challenge for traditional pixel-wise background models which perform poorly when the background has dynamic textures. In this paper, we consider background modelling in a spatial perspective and make an attempt to exploit more information from the outputs of pixel-wise model. We propose a background subtraction scheme using adaptive spatial fusing to refine the output of typical pixel-wise background model — Mixture of Gaussians (MoG) and employ a MRF-MAP scheme to make foreground-background classification using the spatial correlation. Experiments on several challenge sequences show that our method is able to yield significantly better results than the traditional ones and is compelling with existing state of the art background subtraction algorithms. Additionally, we proved our algorithm has linear running time complexity and any pixel-wise background model could be easily integrated into our spatial fusing scheme which greatly enhanced its scalabilities and applications.
Propagation- and Mobility-Aware D2D Social Content Replication

Mobile online social network services have seen rapid expansion; thus, the corresponding huge amounts of user-generated social media contents propagating between users via social connections have significantly challenged the traditional content delivery paradigm. First, replicating all the contents generated by users to edge servers that well “fit” the receivers becomes difficult due to limited bandwidth and storage capacities. Motivated by device-to-device (D2D) communication, which allows users with smart devices to transfer content directly, we propose replicating bandwidth-intensive social contents in a device-to-device manner. Based on large-scale measurement studies on social content propagation and user mobility patterns in edge-network regions, we observe the following: (1) Device-to-device replication can significantly help users download social contents from neighboring peers. (2) Both social propagation and mobility patterns affect how contents should be replicated. (3) The replication strategies depend on regional characteristics (e.g., how users move across regions). Using these measurement insights, we propose a propagation- and mobility-aware content replication strategy for edge-network regions, in which social contents are assigned to users in edge-network regions according to a joint consideration of social graphs, content propagation, and user mobility. We formulate the replication scheduling as an optimization problem and design a distributed algorithm using only historical, local, and partial information to solve it. Trace-driven experiments further verify the superiority of our proposal: compared with conventional pure-movement-based and popularity-based approaches, our design can significantly improve ($2-4$ -fold improvement) the amount of social content successfully delivered via device-to-device replication.
When Data Sponsoring Meets Edge Caching: A Game-Theoretic Analysis

Data sponsoring is a widely-used incentive method in today's cellular networks, where video content providers (CPs) cover part or all of the cellular data cost for mobile users so as to attract more video users and increase data traffic. In the forthcoming 5G cellular networks, edge caching is emerging as a promising technique to deliver videos with lower cost and higher quality. The key idea is to cache video contents on edge networks (e.g., femtocells and WiFi access points) in advance and deliver the cached contents to local video users directly (without involving cellular data cost for users). In this work, we aim to study how the edge caching will affect the CP's data sponsoring strategy as well as the users' behaviors and the data market. Specifically, we consider a single CP who offers both the edge caching service and the data sponsoring service to a set of heterogeneous mobile video users (with different mobility and video request patterns). We formulate the interactions of the CP and the users as a two-stage Stackelberg game, where the CP (leader) determines the budgets (efforts) for both services in Stage I, and the users (followers) decide whether and which service(s) they would like to subscribe to. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically. Our analysis and experimental results show that by introducing the edge caching, the CP can increase his revenue by 105%.
Joint Inter and Intra Shot Modeling for Spectral Video Shot Clustering

This paper proposed a novel video shot clustering algorithm using spectral method by joint modeling of inter and intra shot. Gauss mixture model (GMM) is used for probabilistic space-time modeling of intra-shot pixels. The spectral clustering method is applied on the GMM parameters. The problem of automatic model selection is currently an open issue for spectral method. Here we propose a novel automatic model selection based on joint inter-intra model optimization to achieve the global optimization in both model parameters and cluster numbers. We compare the proposed method with the conventional spectral method for sports video clustering. The simulation results show more accuracy on how many clusters and clustering results
Load Sharing Based on PSO Algorithm for Isolated Distributed Stream Servers

Isolated Distributed Stream Servers (IDSS) is the main form of video-on-demand (VOD) service architecture in industrial community nowadays. Contrast to the previous work on load sharing in distributed VOD system which all follows the idea of having high-speed inner network among service nodes, in this paper, we firstly focus on load sharing algorithm under IDSS architecture and introduce global stream distribution optimization, future user arrival rate estimation and more disk storage redundancy rate to make the effect of load sharing more satisfactory. Moreover the influence of video redundancy rate and some other parameters in our algorithm is detected through simulation. Preliminary experiment results suggest that our algorithm outperforms existing load sharing algorithms under IDSS architecture and sometimes even works better than existing ones with inner network.
A Joint Matrix Factorization Approach to Unsupervised Action Categorization

In this paper, a novel unsupervised approach to mining categories from action video sequences is presented. This approach consists of two modules: action representation and learning model. Videos are regarded as spatially distributed dynamic pixel time series, which are quantized into pixel prototypes. After replacing the pixel time series with their corresponding prototype labels, the video sequences are compressed into 2D action matrices. We put these matrices together to form an multi-action tensor, and propose the joint matrix factorization method to simultaneously cluster the pixel prototypes into pixel signatures, and matrices into action classes. The approach is tested on public and popular Weizmann data set, and promising results are achieved.