nils-herrmann/clean_scirepeval huggingface dataset:
Scenario-Adaptive Rate Allocation for Multi-Source Video Streaming over Ad Hoc Networks

We propose a scenario-adaptive dynamic rate allocation scheme among multiple video sources over an ad hoc network. The most appropriate rate allocation scheme can be selected with a total rate constraint adopting the media access characteristic of an 802.11 network and the selected route for each sender. The selected scheme optimizes the rate-distortion. The scheme is performed at the receiver of the video streams and the decision is based on a simple rate-distortion model of video and a fast algorithm to classify the activity level of the video content. Experimental results for a multi-source surveillance show that the proposed scheme outperforms the conventional fixed quality scheme and can adapt to different scenarios by adjusting the parameter b dynamically
A Novel Distributed and Practical Incentive Mechanism for Peer to Peer Live Video Streaming

The successful deployment of peer-to-peer (P2P) live video streaming systems has practically demonstrated that it can scale to reliably support a large population of peers. However, peers, representing rational end users, tend to be non-cooperative when it comes to the duty rather than the self-interests, running counter to the fundamental design philosophy of P2P concept. The objective of this paper is to investigate the problem of encouraging users to balance what they take from the system with what they contribute. We first make a statistical analysis to the service logs of a practical P2P live video streaming system and reveal intrinsic characteristic of users' online duration. Second, we thus propose a novel incentive mechanism based on the composite contributions which consist of two objective metrics, i.e. the on-line duration and effective upstream traffic. This mechanism offers service differentiation to users with different contributions and has some desirable properties: (1) distributed nature upon gossip-based overlay structure and (2) practical oriented evaluation criteria. The experiment results over PlanetLab further verify its effectiveness
Statistics in bilateral domain: Novel statistics of natural images

Recently, there has been a great deal of interest in statistics of natural images in linear domain. However, a detail study of image statistics in non-linear domain on a very large dataset is still missing. In this paper, we analyze natural images' statistics in “bilateral domain” on different sub bands which are generated by the classical bilateral filter. Compared with those in linear domain, the statistics in bilateral domain are significantly superior in generalization, de-correlation, and local feature extraction. We also observe some entirely new but interesting features.
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment

Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.
Characterizing viewing engagement patterns in DASH

Dynamic adaptive streaming over HTTP (DASH) has seen a rapid increase in recent years. While opening a door for ensuring smooth video playback with bitrate adaptation, especially for unstable networks, DASH also requires a set of new streaming strategies (e.g., bitrate adaptation). A key of designing these strategies is to understand the viewing engagement patterns in DASH, which are the correlation between streaming strategies and Quality of Experience (QoE) of users. To characterize viewing engagement patterns, we use large-scale traces from a DASH streaming provider, based on which we measure users' viewing patterns in DASH sessions, and the contextual factors (e.g., streaming device) that affect the viewing engagement. Based on our measurement insights, we further design a viewing engagement prediction model, to let streaming providers predict how long viewers will remain in DASH sessions given the streaming contexts. The effectiveness of the prediction model is verified by the real traces collected.
DFGNet: Mapping dataflow graph onto CGRA by a deep learning approach

The coarse-grained reconfigurable architecture (C-GRA) is a promising platform that provides both high performance and high power-efficiency. Dataflow graph (DFG) mapping is critical to tap the potentials of CGRAs. Inspired from the great progress made in tree search game using deep neural network, we proposed a frame work for learning convolutional neural network for mapping DFGs onto spatial programmable CGRAs. Considering the mapping process, we present a dual-input neural network capturing the features from both DFGs in applications and Process Element Array (PEA) in CGRA. In order to train the neural network, algorithms are designed to automatically generate a data set from PEA intermediate states of preprocessed DFG. Finally, experimental results demonstrate that our proposed mapping approach is competitive with state-of-the-art DFG mapping algorithms in performance while the compilation time is greatly reduced.
Peer-assisted online games with social reciprocity

Online games and social networks are cross-pollinating rapidly in today's Internet: Online social network sites are deploying more and more games in their systems, while online game providers are leveraging social networks to power their games. An intriguing development as it is, the operational challenge in the previous game persists, i.e., the large server operational cost remains a non-negligible obstacle for deploying high-quality multi-player games. Peer-to-peer based game network design could be a rescue, only if the game players' mutual resource contribution has been fully incentivized and efficiently scheduled. Exploring the unique advantage of social network based games (social games), we advocate to utilize social reciprocities among peers with social relationships for efficient contribution incentivization and scheduling, so as to power a high-quality online game with low server cost. In this paper, social reciprocity is exploited with two give-and-take ratios at each peer: (1) peer contribution ratio (PCR), which evaluates the reciprocity level between a pair of social friends, and (2) system contribution ratio (SCR), which records the give-and-take level of the player to and from the entire network. We design efficient peer-to-peer mechanisms for game state distribution using the two ratios, where each player optimally decides which other players to seek relay help from and help in relaying game states, respectively, based on combined evaluations of their social relationship and historical reciprocity levels. Our design achieves effective incentives for resource contribution, load balancing among relay peers, as well as efficient social-aware resource scheduling. We also discuss practical implementation concerns and implement our design in a prototype online social game. Our extensive evaluations based on experiments on PlanetLab verify that high-quality large-scale social games can be achieved with conservative server costs.
Understanding the Power of Pull-Based Streaming Protocol: Can We Do Better?

Most of the real deployed peer-to-peer streaming systems adopt pull-based streaming protocol. In this paper, we demonstrate that, besides simplicity and robustness, with proper parameter settings, when the server bandwidth is above several times of the raw streaming rate, which is reasonable for practical live streaming system, simple pull-based P2P streaming protocol is nearly optimal in terms of peer upload capacity utilization and system throughput even without intelligent scheduling and bandwidth measurement. We also indicate that whether this near optimality can be achieved depends on the parameters in pull-based protocol, server bandwidth and group size. Then we present our mathematical analysis to gain deeper insight in this characteristic of pull-based streaming protocol. On the other hand, the optimality of pull-based protocol comes from a cost -tradeoff between control overhead and delay, that is, the protocol has either large control overhead or large delay. To break the tradeoff, we propose a pull-push hybrid protocol. The basic idea is to consider pull-based protocol as a highly efficient bandwidth-aware multicast routing protocol and push down packets along the trees formed by pull-based protocol. Both simulation and real-world experiment show that this protocol is not only even more effective in throughput than pull-based protocol but also has far lower delay and much smaller overhead. And to achieve near optimality in peer capacity utilization without churn, the server bandwidth needed can be further relaxed. Furthermore, the proposed protocol is fully implemented in our deployed GridMedia system and has the record to support over 220,000 users simultaneously online.
On deployment of overlay network for live video streaming

Overlay network has emerged as a popular alternative to traditional client-server architecture for the distribution of media content. Intrinsic to the notion is a 'self-growing' community of end users. It is thus important to investigate practical issues in reality rather than following prescribed theoretical assumptions. This paper presents our research experiences on deployment of overlay network for live video streaming over Internet. In our implementation we adopt a gossip-based overlay construction protocol to accommodate topology changes and a composite scheduling mechanism to stream video contents. More importantly, with the service to totally more than 500,000 users and maximum 15,239 concurrent users provided by one common streaming server, we then exhibit insightful statistical results to reveal system performance and nodes properties in terms of capacity evolution, quality of service, request rate and online duration
Learning-based quality assessment of retargeted stereoscopic images

Stereoscopic image retargeting techniques aim to flexibly display 3D images with different aspect ratios and simultaneously preserve salient regions and comfortable depth perception. Various stereoscopic image retargeting techniques have been proposed recently. However, there is still no effective objective metric for visual quality assessment of retargeted stereoscopic images. In this paper, we build a stereoscopic image retargeting database and propose a learning-based objective method to evaluate the stereoscopic image retargeting quality. The perception quality of the database are evaluated by subjects. We extract new features of quality assessment and fuse them to assess stereoscopic image retargeting quality using neural network. Experiments conducted with above-mentioned database confirm the effectiveness of the proposed method. The results show the good consistency between the objective assessments and subjective rankings.
A Survey of Cloudlet Based Mobile Computing

Nowadays, mobile devices are ubiquitous in people's everyday life and applications on mobile devices are becoming increasingly resource-hungry. However, the resources on mobile devices are limited. Mobile cloud computing addresses the resource scarcity problem of mobile devices by offloading computation and/or data from mobile devices into the cloud. In the converging progress of mobile computing and cloud computing, the cloudlet is an important complement to the client-cloud hierarchy. This paper presents an extensive survey of researches on cloudlet based mobile computing. We first retrospect the evolution of cloudlet based mobile computing. After that, we review the existing works on cloudlet based computation offloading and data offloading. Then we introduce two examples of commercial cloudlet products. At last, we discuss the current situation, the challenges and future directions of this area.
Dynamic range expansion of video sequences: A subjective quality assessment study

The goal of this paper is to assess whether existing solutions to expand low dynamic range (LDR) still images to high dynamic range (HDR) displays can apply to moving pictures. To this end, we conduct a subjective quality assessment test on four state-of-the-art expansion operators, using high-quality input LDR content. Our results show that, perhaps surprisingly, no temporal artifacts, such as flickering due to global illumination changes, are introduced applying frame-based algorithms. In addition, they confirm previous studies in that operators as simple as linear expansion can yield acceptable (or even excellent) quality of the HDR video.
Learning-based tone mapping operator for image matching

In this paper, we propose a new framework to optimally tone-map a high dynamic range (HDR) content for image matching under drastic illumination variations. This task is of fundamental importance for many computer vision applications. To design such a framework, we build a luminance invariant guidance model using a Support Vector Regressor (SVR) and learn it to facilitate the extraction of invariant descriptors from scenes subject to wide variety of appearance changes such as day/night transition. To this end, we initially generate appropriate training samples using a simple similarity-maximization mechanism. We then employ the learned model to predict optimal modulation maps that help to locally alter the intrinsic characteristics (such as shape, size) of the tone mapping function. We evaluate the proposed model performance in terms of matching score and mean average precision rate using state-of-the-art descriptor extraction schemes. We demonstrate that our tone mapping framework significantly outperforms the existing perceptually-driven state-of-the-art TMOs on the benchmark datasets.
A dual modulation algorithm for accurate reproduction of high dynamic range video

In this paper, we consider the problem of accurately reproducing high dynamic range (HDR) images and video on HDR displays. Differently from conventional image reproduction devices, HDR displays require dual modulated signals to drive the back LED panel and the front LCD screen. In this work, we present an iterative scaling approach to estimate these two signals, which is able to reproduce accurately luminance values stored in HDR files. To avoid temporal artifacts due to frame-by-frame processing, we also describe a temporal smoothing mechanism to compute backlight illumination. Our results demonstrate higher accuracy than simpler approaches such as built-in display rendering. Furthermore, the proposed method offers the possibility to estimate precisely the luminance emitted by the display.
Statistical analysis of residue signal for backward compatible HDR image coding

Backward compatibility to the existing 8-bit displays for HDR image and video compression is an essential requirement for the wide acceptance of HDR technology by the users. While these previous methods mainly focus on the rate-distortion optimization of base layer, the encoding of the residue in the enhancement layer comparatively has received less attention. In this paper, we investigate whether HDR image coding residue exhibits also local anisotropic characteristics or not as in the case of motion compensated LDR video coding residue. The verification of existence of such characteristics, as an essential initial stage in such a research, form a base for the development of directional encoding methods for HDR residue as well. For this purpose, statistical auto-covariance analysis on HDR image coding residue is performed in this paper. Specifically, we first interpret the resulting auto-covariance model parameters for HDR images and then the behavior of these parameters is analyzed with respect to bitrate, spatial activity and dynamic range as the main variables in HDR image coding. The experiments indicate that the HDR residue exhibits local anisotropic characteristics, which enables the use of directional methods for the encoding of enhancement layer in HDR coding.
Good features to track for RGBD images

RGBD (texture-plus-depth) image representation enriches traditional 2D content with additional geometrical information, having the potential to improve the performance of many computer vision tasks. In image matching, this has been partially studied by considering how depth maps can help render feature descriptors more distinctive. However, little has been done to design keypoint detection approaches able to leverage the availability of depth information. In this paper, we propose a novel and robust approach for detecting corners from RGBD images. Our method modifies a classical corner detection strategy, based on local second-order moment matrices, by computing derivatives in a coordinate system which reflects the local properties of object surfaces. Our results demonstrate a higher stability to out-of-plane rotations of the proposed RGBD corner detector both in terms of feature repeatability and in a visual odometry application.
Improving distinctiveness of brisk features using depth maps

Binary local descriptors are widely used in computer vision thanks to their compactness and robustness to many image transformations such as rotations or scale changes. However, more complex transformations, like changes in camera viewpoint, are difficult to deal with using conventional features due to the lack of geometric information about the scene. In this paper, we propose a local binary descriptor which assumes that geometric information is available as a depth map. It employs a local parametrization of the scene surface, obtained through depth information, which is used to build a BRISK-like sampling pattern intrinsic to the scene surface. Although we illustrate the proposed method using the BRISK architecture, the obtained parametrization is rather general and could be embedded into other binary descriptors. Our simulations on a set of synthetically generated scenes show that the proposed descriptor is significantly more stable and distinctive than popular BRISK descriptors under a wide range of viewpoint angle changes.
Blind High Dynamic Range Quality estimation by disentangling perceptual and noise features in images

No reference image quality assessment(NR-IQA) on true High Dynamic Range (HDR) images is an unexplored and interesting research topic that has become relevant with the current boom in HDR technology. We first conduct a study on the performance of existing algorithms for NR-IQA, originally developed for Low Dynamic Range images(LDR), on HDR data. Using the results of our investigation, we then propose a new convolutional architecture for NR-IQA achieving state of the art performance in terms of correlation to human judgment on quality. The proposed model disentangles perceptual effects exhibited by the distorted image from the noise present in the image and is capable of extracting perceptually relevant features without the necessity of handcrafting the features. Our architecture predicts the amount and location of the visual errors present in the distorted images without the reference image and performs comparably to the state of the art Full Reference Image Quality assessment(FR-IQA) algorithms.
An iterative algorithm for image rendering on High Dynamic Range displays

Recent advancements in display technologies created possibility for production of High Dynamic Range (HDR) displays. These HDR displays are capable of generating high luminance values up to 4000-6000 nits where conventional Low Dynamic Range (LDR) displays can generate luminance values between 100-500 nits. This increase in luminance values have different implications on human visual perception and the quality of the observed material. This study presents an iterative algorithm for image rendering on dual modulation HDR displays in order to both create and estimate accurate luminance values. In order to understand how it works, the characteristics properties of the SIM2 HDR47 display has been analyzed in detail. The details of the developed iterative algorithm have been explained, and results have been presented. Several measurements have been made to validate the proposed rendering algorithm. The results show that the proposed algorithm estimates the emitted luminance accurately and gives more accurate results when compared to built-in rendering algorithm of SIM2 HDR47 display.
Optimizing tone mapping operators for keypoint detection under illumination changes

Tone mapping operators (TMO) have recently raised interest for their capability to handle illumination changes. However, these TMOs are optimized with respect to perception rather than image analysis tasks like key point detection. Moreover, no work has been done to analyze the factors affecting the optimization of TMOs for such tasks. In this paper, we investigate the influence of two factors-Correlation Coefficient (CC) and Repeatability Rate (RR) of the tone mapped images for the optimization of classical Retinex based models to enhance key point detection under illumination changes. CC-based optimized models aim at increasing the similarity of the tone mapped images. Conversely, RR-based optimized models quantify the optimal detection performance gains. By considering two simple Retinex based models, i.e., Gaussian and bilateral filtering, we show that estimating as precisely as possible the illumination, CC-based optimized models do not necessarily bring to optimal key point detection performance. We conclude that, instead, other criteria specific to RR-based optimized models should be taken into account. Moreover, large gains in performance with respect to existing popular TMOs motivate further research towards optimal tone mapping technique for computer vision applications.
Keypoint Detection in RGBD Images Based on an Anisotropic Scale Space

The increasing availability of texture+depth (RGBD) content has recently motivated research toward the design of image features able to employ the additional geometrical information provided by depth. Indeed, such features are supposed to provide higher robustness than conventional 2D features in the presence of large changes of camera viewpoint. In this paper, we consider the first stage of RGBD image matching, i.e., keypoint detection. In order to obtain viewpoint-covariant keypoints, we design a filtering process, which approximates a diffusion process along the surfaces of the scene, by means of the information provided by depth. Next, we employ this multiscale representation to find keypoints through a multiscale keypoint detector. The keypoints obtained by the proposed detector provide substantially higher stability to viewpoint changes than alternative 2D and RGBD feature extraction approaches, both in terms of repeatability and image classification accuracy. Furthermore, the proposed detector can be efficiently implemented on a GPU.