Emerging single-photon camera (SPC) technologies have unique challenges in data acquisition and processing. Unlike conventional sensors that produce a single 8- to 16-bit brightness value per pixel, SPCs record photon arrivals with many more samples per pixel, using high floating-point precision for each photon collected. This means that they must handle potentially millions of timestamps, especially at higher spatial resolutions and in the presence of ambient light, creating bottlenecks within the pixel circuitry. To address these challenges associated with SPCs, this paper proposes adaptive algorithms designed to efficiently distribute hardware resources among groups of pixels. By selectively subsampling the data entering the sensor, the proposed approach significantly reduces system load and opens new paths toward high-quality HDR imaging using SPCs at high spatial resolutions.

Traditional video compression continues to advance, but the gains in efficiency are diminishing and come at the cost of higher computational complexity. Despite achieving competitive rate-distortion results, current neural video codecs (NVCs) generally lack support for a wide range of quality levels, often requiring multiple models to achieve flexible rate control, which increases both training cost and deployment complexity. To address the limitations of both traditional codecs and current NVCs, we propose a hybrid video compression framework that integrates traditional codecs with hint-guided video frame interpolation (VFI), a learning-based technique for synthesizing intermediate frames. By using decoded reference frames and leveraging compressed-domain hints to guide interpolation, our method improves both motion compensation and reconstruction quality. This design combines the efficiency of traditional codecs with the adaptability of neural interpolation, achieving consistent rate-distortion performance and supporting a wide range of quality levels on standard benchmarks.

In this paper, we show that exploiting Generative Adversarial Networks (GANs) to transform nighttime images into daytime representation increases the robustness of pedestrian detection in low-light conditions. Our work aims at first learning the image translation to transfer the style from daytime images to nighttime images with unpaired GAN training. Second, we use our end-to-end trained GAN model to translate night images as a pre-processing step before feeding them into an object detector that is pre-trained on daytime images only. To demonstrate the effectiveness of our translation approach, we conducted experiments on two real-world pedestrian datasets using both one-stage and two-stage object detectors. Our results outperform the baseline in all experiments and show highly competitive detection performance compared with other GAN-based approaches while holding the most lightweight architecture. We believe that our approach is an effective pre-processing first step that helps in bridging the performance gap between day and night at no expense of re-training object detector networks with more night images.

Despite major advancements in state-of-the-art object detectors and low-light image enhancements, nighttime pedestrian detection remains a challenge. One commonly used solution to remedy this domain shift problem is re-training object detectors with extra nighttime scenes which is not only computationally expensive but also not generalizable. In this paper, we explore a new solution and aim to systematically analyze and understand the efficiency of a lightening algorithm on pedestrian detection in low-light conditions. In our analysis, we first explore the effect of normalizing image luminance based on the ground truth bounding boxes to adaptively adjust global image luminance and evaluate its effects on detection performance. Second, unlike general low-light image enhancements that rely on global or local image statistics, we design a pedestrian-luminance-aware lightening algorithm to automatically correct nighttime images luminance so that pedestrians can be more robustly detected. Through extensive experiments, our algorithm not only achieves competitive detection results compared to the baseline on two real-world nighttime datasets but also elevates the confidence score of detected pedestrians.

The ability to capture and distribute digital videos has been available for many years. Users can easily capture high-quality video streams with mobile devices and distribute them to end users through varying platforms. This paper presents the design and implementation of a new multimedia framework called Video as Text (vText), which analyzes and manipulates video data as trivially as we handle text data in most Unix and Linux systems. In most Unix systems, it is easy to accomplish highly complex textual analysis and processing by combining relatively simple programs (e.g., grep, awk, sed, and cut) through Unix pipes. The vText paradigm seeks to mimic such programs. We demonstrate the design and implementation of vText linking video codecs with computer vision and image-processing algorithms. The experimental results indicate that the combination of simple programs provides high flexibility to users but does not incur high overhead when processing the video data.

Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.

Abstract Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy. Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation-based approaches as a privacy defense against automated face recognition. Specifically, we first identify practical requirements for such approaches and then propose two practical adversarial perturbation approaches – (i) learned universal ensemble perturbations (UEP), and (ii) k-randomized transparent image overlays (k-RTIO) that are semantic adversarial perturbations. We demonstrate how users can generate effective transferable perturbations under realistic assumptions with less effort. We evaluate the proposed methods against state-of-theart online and offline face recognition models, Clarifai.com and DeepFace, respectively. Our findings show that UEP and k-RTIO respectively achieve more than 85% and 90% success against face recognition models. Additionally, we explore potential countermeasures that classifiers can use to thwart the proposed defenses. Particularly, we demonstrate one effective countermeasure against UEP.

In this paper, we present a hybrid video compression technique that combines the advantages of residual coding techniques found in traditional DCT-based video compression and learning-based video frame interpolation to reduce the amount of residual data that needs to be compressed. Learning-based frame interpolation techniques use machine learning algorithms to predict frames but have difficulty with uncovered areas and non-linear motion. This approach uses DCT-based residual coding only on areas that are difficult for video interpolation and provides tunable compression for such areas through an adaptive selection of data to be encoded. Experimental data for both PSNR and the newer video multi-method assessment fusion (VMAF) metrics are provided. Our results show that we can reduce the amount of data required to represent a video stream compared with traditional video coding while outperforming video frame interpolation techniques in quality.

Deep Convolutional Neural Networks (DCNNs) are vulnerable to images that have been altered with well-engineered and imperceptible perturbations. We propose three color quantization pre-processing techniques to make DCNNs more robust to adversarial perturbation including Gaussian smoothing and PNM color reduction (GPCR), color quantization using Gaussian smoothing and K-means (GK-means), and fast GK-means. We evaluate the approaches on a subset of the ImageNet dataset. Our evaluation reveals that our GK-means-based algorithms have the best top-1 accuracy. We also present the trade-off between GK-means-based algorithms and GPCR with respect to computational time.

In computer vision, scale-invariant feature transform (SIFT) remains one of the most commonly used algorithms for feature extraction, but its high computational cost makes it hard to deploy in real-time applications. In this paper, we introduce a novel technique to restructure the inter-octave and intra-octave dependencies of SIFT's keypoint detection and description processes, allowing it to be stopped early and produce approximate results in proportion to the time for which it was allowed to run. If our algorithm is run to completion (about 0.7% longer than traditional SIFT), its results and SIFT's converge. Unlike previous approaches to real-time SIFT, we require no special hardware and make no compromises in keypoint quality, making our technique ideal for real-time and near-real-time applications on resource-constrained systems. We use standard data sets and metrics to analyze the performance of our algorithm and the quality of the generated keypoints.

As video-based sensor networks continue to scale and become more ubiquitous, it is becoming increasingly important to focus systems research on techniques that support content-based decisions in real-time towards the edge of the network. While some prior work has focused on high-level image and video quality's effect on computer vision (e.g., object recognition). We are unaware of any work that focuses on the low-level details of why. This paper explores the impact of compression on underlying computer vision techniques. Specifically, this paper focuses on understanding the fundamental impact of compression on SIFT feature detection and matching. We show how reduced resolution or frame quality can negatively impact feature detection and tracking.

No abstract available.

With more and more data being stored in the cloud, securing multimedia data is becoming increasingly important. Use of existing encryption methods with cloud services is possible, but makes many web-based applications difficult or impossible to use. In this paper, we propose a new image encryption scheme specially designed to protect JPEG images in cloud photo storage services. Our technique allows efficient reconstruction of an accurate low-resolution thumbnail from the ciphertext image, but aims to prevent the extraction of any more detailed information. This will allow efficient storage and retrieval of image data in the cloud but protect its contents from outside hackers or snooping cloud administrators. Experiments of the proposed approach using an online selfie database show that it can achieve a good balance of privacy, utility, image quality, and file size.

This paper describes a synthetically-generated, multi-lens stereoscopic video dataset and associated 3D models. Creating a multi-lens video stream requires small inter-lens spacing. While such cameras can be built out of off-the-shelf parts, they are not "professional" enough to allow for necessary requirements such as zoom-lens control or synchronization between cameras. Other dedicated devices exist but do not have sufficient resolution per image. This dataset provides 20 synthetic models, each with an associated multi-lens walkthrough, and the uncompressed video from its generation. This dataset can be used for multi-view compression, multi-view streaming, view-interpolation, or other computer graphics related research.

In this paper, we propose a framework to support the bridging of applications and computer-vision based sensor networks. We argue that the semantic gap, the difference between the data collected in a sensor network and the information needed by the application, in video-based sensor networks can only be addressed by providing systems support in such a way that allows users and computing systems to meet in the middle. We first outline the vision of the system that we are working towards. We then describe initial experiments that we have conducted using a functional component of the system applied to real-world video data that is being collected by intelligent transportation systems researchers.

The notion of streaming media has been around for nearly two decades. From a research novelty to now consuming over half of all Internet bandwidth, streaming media is now much more commonplace. This keynote address will give my perspective on the history and evolution of streaming media and lessons learned. Since the introduction of digital video in the early 1990s, our community has spent significant effort focused on the development of scalable and adaptive mechanisms for streaming media. We have witnessed techniques ranging from interactive multimedia streaming, stored video streaming, peer-to-peer streaming, and remote tele-presence. Several common themes have emerged in these technologies and as we move forward as a community, this keynote will offer some thought and observations on applying lessons learned to future technologies and a handful of areas where streaming media can make significant impact beyond where we are today.

As we move toward more ubiquitous stereoscopic video, particularly with multiple (> 2) lenses, the need to understand the efficiency of compression will become increasingly important. In this paper, we explore the impact of spatial (between lenses) and temporal (over time) compression for stereoscopic video images. In particular, because stereoscopic images are taken at the same time, there is expected to be a high correlation between pixels in the horizontal direction due to the fixed nature of the multiple lenses. We propose a vertically reduced search window in order to take advantage of this correlation. Starting with multiple stereoscopic video sequences shot using a production studio 3D camera, we explore the effectiveness of temporal and inter-lens motion compensation for stereoscopic video. Furthermore, the experiments use exhaustive search to remove the effects of heuristic-based motion-compensation techniques.

Stereoscopic displays are becoming ubiquitous, ranging from large 3-D TVs to small mobile phones. Stereoscopic photos need to be carefully adapted to be effectively viewed on the displays other than originally intended. In this paper, we present a method that can automatically crop and scale an existing stereoscopic photo to a variety of displays while preserving its aesthetic value. We formulate stereoscopic photo adaptation as an optimization problem that aims to preserve the aesthetic value of the input photo. We define a wide range of energy terms to preserve the stereoscopic photo aesthetics by borrowing rules from stereoscopic photography. Our experiments on a wide variety of stereoscopic photos demonstrate that our method can robustly produce display-dependent stereoscopic photos that deliver pleasant viewing experiences.

Warping is one of the basic image processing techniques. Directly applying existing monocular image warping techniques to stereoscopic images is problematic as it often introduces vertical disparities and damages the original disparity distribution. In this paper, we show that these problems can be solved by appropriately warping both the disparity map and the two images of a stereoscopic image. We accordingly develop a technique for extending existing image warping algorithms to stereoscopic images. This technique divides stereoscopic image warping into three steps. Our method first applies the user-specified warping to one of the two images. Our method then computes the target disparity map according to the user specified warping. The target disparity map is optimized to preserve the perceived 3D shape of image content after image warping. Our method finally warps the other image using a spatially-varying warping method guided by the target disparity map. Our experiments show that our technique enables existing warping methods to be effectively applied to stereoscopic images, ranging from parametric global warping to non-parametric spatially-varying warping.

As sensor networking technologies continue to develop, the notion of adding large-scale mobility into sensor networks is becoming feasible by crowd-sourcing data collection to personal mobile devices. However, tasking such networks at fine granularity becomes problematic because the sensors are heterogeneous and owned by users instead of network operators. In this paper, we present Zoom, a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. Zoom allows users to define arbitrary sensor groupings over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. The key idea is the separation of the task information ( what task a particular sensor should perform ) from the task implementation ( code ). Zoom consists of (i) a map, an overlay on top of a geographic region, to represent both the sensor groups and the task information, and (ii) adaptive encoding of the map at multiple resolutions and region-of-interest cropping for resource-constrained devices, allowing sensors to zoom in quickly to a specific region to determine their task. Simulation of a realistic traffic application over an area of 1 sq. km with a task map of size 1.5 KB shows that more than 90% of nodes are tasked correctly. Zoom also outperforms Logical Neighborhoods, the state-of-the-art tasking protocol in task information size for similar tasks. Its encoded map size is always less than 50% of Logical Neighborhood's predicate size.

In this paper, we propose a content-based, threaded stereoscopic video compression algorithm. We believe that it is likely future stereoscopic imaging systems will contain more than two lenses, allowing the display system to optimize the stereoscopic viewing experience. Furthermore, it will allow for the adjustment of framing (scene) composition errors that can arise from stereoscopic capture. Our proposed system uses 10 linearly aligned lenses. Upon capture, the images are run through a feature detection and matching algorithm in order to determine disparity between the stereoscopic images. During compression, the disparity measurements can be used to drive the selection of key frames within the image sets to provide better retrieval of data for display.

Current sensor networks have limited use after deployment due to inability to dynamically update sensing tasks in the networks. Assigning multiple tasks to multiple sensor groups in a sensor network deployed over a large geographic region at fine granularity is problematic because the sensors are heterogeneous, potentially mobile, and owned by users instead of network operators. We demonstrate Zoom [1], a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. The key idea in Zoom is decoupling the task specification from the task implementation using a spatial 2-dimensional representation of the tasking region (e.g., maps). Zoom allows users to define sensor groups over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. To the best of our knowledge, this is the first work to propose a map based approach for mobile sensing systems.

The ability to create super high-resolution video is becoming relative easy to do either through a single high-definition video camera or panoramic video that automatically stitches multiple views together. As an example of the former, the motion picture industry now has 6000 × 4000 pixel full-rate video cameras available. This means that supporting region-of-interest cropping will become more important in the future. In this article, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression-compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Fortunately, video standards like MPEG-2 specify the format of a compliant stream, and not the algorithm to get there. As a result, there is an opportunity to allow system researchers and implementers ways to optimize for applications. We show various fundamental tradeoffs that are made in order to support region-of-interest cropping with super high-resolution video which we received from a local motion-picture firm.

Current feature tracking frameworks in sensor networks exploit advantages of either mobility, where mobile sensors can provide micro scale information of a small sensing area or numerical models that can provide macro scale information about the environment but not both. With the continual development of sensor networks, mobility becomes an important feature to integrate next generation sensing systems. In addition, recent advances in environmental modeling also allow us to better understand basic behavior of the environment. In order to further improve existing sensing systems, we need a new framework that can take advantages of existing fixed sensor networks, mobile sensors and numerical models. We develop CoTrack, a Collaborative Tracking framework, that allows mobile sensors to cooperate with fixed sensors and numerical models to accurately track dynamic features in an environment. The key innovation in CoTrack is the incorporation of numerical models at different scales and sensor measurements to guide mobile sensors for tracking. The framework includes three components: a macro model for large-scale estimation, a micro model for locale estimation of specific features based on sensor measurements, and an adaptive sampling scheme that guides mobile sensors to accurately track dynamic features. We apply our framework to track salinity intrusion in the Columbia River estuary in Oregon, United States. Our framework is fast and can reduce tracking error by more than half compared to existing data assimilation and state-of-the-art numerical models.

For many applications in mobile wireless ad hoc networks (MANETs), forming an end-to-end data path is not always necessary; instead, the primary routing goal is often data collection or dissemination where only a data source is known. Routing algorithms must be carefully chosen in order to suit the needs of applications employing them. Our focus is on data collection applications in MANETs where limited mobility information is required to route data in a scalable manner. To address this goal, we employ the concepts of delay-tolerant networking (DTN) in which data makes progress toward a destination with high latency expectations and little knowledge of routing topology. Specifically, we present time-to-network (TTN) forwarding, a method of forwarding data generated by mobile nodes to a network endpoint in such a way that delivery latency is lowered without high networking cost. By segmenting mobility patterns into trips, we are able to apply TTN to a vehicular network using only an estimated destination arrival time for each vehicle. We evaluate TTN using mobility data from the TRANSIMS simulator for a real road network. Results show that our algorithm produces collection-to-network latencies similar to more generic algorithms but at a lower cost and with higher efficiency. Furthermore, we establish a lower bound for delivery latency in our experiments and compare it to TTN. This also helps normalize the interpretation of results specific to our mobility model.

For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3-D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3-D feature point-rich target to all cameras, then using the known geometry of a 3-D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3-D target's feature points. As each 3-D target position establishes a distinct coordinate frame, cameras that view more than one 3-D target position compute translations and rotations relating different positions' coordinate frames and share the transform data with neighbors to facilitate realignment of all cameras to a single coordinate frame. Compared to other localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it requires communication only to determine simultaneous target viewings and for passing transform data. Additionally, our solution requires only pairwise view overlaps of sufficient size to see the 3-D target and detect its feature points, while also giving camera positions in meaningful units. We evaluate our algorithm in both real and simulated smart camera networks. In the real network, position error is less than 1 ('') when the 3-D target's feature points fill only 2.9% of the frame area.

The ability to create very high-resolution video is becoming relative easy to do today, either through a single high definition video camera or panoramic video stitched from multiple cameras. This means that supporting region-of-interest cropping will become more important in the future. In this paper, we propose a mechanism to support region-of-interest adaptation of stored video. The proposed approach creates a compression compliant stream (e.g., MPEG-2), while still allowing it to be cropped. Using data from a motion-picture industry video camera, we show that the proposed approach, while adding a small amount of overhead, can be used to efficiently crop large resolution video into a smaller stream.

The Columbia River (CoRie) Observation Network includes an extensive array of fixed stations monitoring the Columbia River estuary and nearby coastal ocean. At each station, variable combinations of in-situ sensors measure one or more physical properties of water or atmosphere. Using a multi-scale data assimilation model, the CORIE modeling system integrates models and field controls to produce a simulation of 3D circulation, in a region centered in the estuary and plume. The CORIE data assimilation framework [1] combines observational data with numerical data models to produce an estimated system state for the physical process. To augment the fixed observational network, additional data is collected during periodical cruises of a mobile sensor station. Because these cruises are expensive and rare, an important goal for scientists is to sample data at points that most reduce the uncertainty of the data assimilation model. This is challenging, since the estuary environment is very dynamic, and therefore the optimal cruise path cannot be determined in advance. The goal of our system is to move the mobile station as the data assimilation proceeds in order to maximally reduce the uncertainty in the data assimilation process.

As video sensor networks become more widely deployed, mechanisms for adaptively transmitting video data within the network are necessary because of their generally large resource requirements compared to their scalar counterparts. The key features of such networks include (i) many sources can inject video into the network that is destined for the same sink and (ii) nodes that participate in routing can also potentially work collaboratively for the benefit of the entire system. In this paper, we propose a multi-hop buffering and adaptation framework for video-based sensor networking applications. We explore several approaches in this framework and compare their performance with traditional IP-based video streaming technologies. Our experiments show that these approaches outperform traditional technologies in video quality, bandwidth waste, and bandwidth sharing fairness.

This demonstration shows a powerful high-level, heterogeneous sensor networking framework, Cascades. We intend to demonstrate how, with this framework, application designers have great control over implementation designs without the requirement of in-depth development. Several key components and example applications will be demonstrated, along with some important concepts used in this Python-based framework. The emphasis will be on video sensing and application control in heterogeneous sensor networks consisting of Crossbow Stargate (PC-class) and MicaZ (mote-class) devices.

Client puzzles have been proposed in a number of protocols as a mechanism for mitigating the effects of distributed denial of service (DDoS) attacks. In order to provide protection against simultaneous attacks across a wide range of applications and protocols, however, such puzzles must be placed at a layer common to all of them; the network layer. Placing puzzles at the IP layer fundamentally changes the service paradigm of the Internet, allowing any device within the network to push load back onto those it is servicing. An advantage of network layer puzzles over previous puzzle mechanisms is that they can be applied to all traffic from malicious clients, making it possible to defend against arbitrary attacks as well as making previously voluntary mechanisms mandatory. In this paper, we outline goals which must be met for puzzles to be deployed effectively at the network layer. We then describe the design, implementation, and evaluation of a system that meets these goals by supporting efficient, fine-grained control of puzzles at the network layer. In particular, we describe modifications to existing puzzle protocols that allow them to work at the network layer, a hint-based hash-reversal puzzle that allows for the generation and verification of fine-grained puzzles at line speed in the fast path of high-speed routers, and an iptables implementation that supports transparent deployment at arbitrary locations in the network.

Cheating in on-line games is a prevalent problem for both game makers and players. The popular real-time strategy game genre is especially vulnerable to cheats, as it is frequently hosted as a peer-to-peer game. As the genre has moved towards a distributed simulation approach to gameplay, the number of cheats has been reduced to bug exploits and “maphacks”: a form of information exposure that reveals the opponent’s units and positions when they should be hidden. This paper proposes a technique for detecting maphacking based on bit commitment and explores the tradeoffs in network traffic and information exposure inherent in reducing information exposure in peer-to-peer games.

Complex heterogeneous sensor networks that include both traditional scalar and richer video data are becoming possible. With such technologies comes the task of making them useful to the applications that rely on them. Unfortunately, such applications are extremely diverse in the interaction requirements of the system components. This paper argues that the system software for such sensor systems needs to balance optimization for performance and providing a flexible interface that can be tailored to the requirements of the user application. We propose Cascades, a cascading filter mechanism that can allow users to quickly compose and re-task diverse sensor systems (which include both scalar and video-based sensors) with highly optimized application-specific algorithms.

A wireless network of remotely located, self-powered video sensors must make efficient use of limited network capacity and power. An ideal media-access protocol for such a network would fully utilize available throughput while using no more power than necessary and maintaining a reasonable level of fairness. To accomplish these goals, the paper proposes and evaluates a time-division multiple-access protocol that may run on top of unmodified IEEE 802.11 hardware. Our protocol uses an 802.11 access point as a controller for a set of 802.11 clients. The controller allocates timeslots to its clients; during its timeslot, a client may send to and receive from the controller. Each client may turn its radio off during other clients' timeslots to save power that would otherwise be wasted contending for a busy channel. We compare and contrast our protocol to other wireless power-saving mechanisms, then discuss the design and implementation of our protocol and its performance compared to standard 802.11 in terms of throughput, power, and fairness. We conclude that our protocol offers a significant reduction in power consumption and improved fairness. Finally, we show that our protocol's throughput is similar to that of standard 802.11.

Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This article describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe two video sensor platforms that can deliver high-quality video over 802.11 networks with a power requirement less than 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive long-periods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system.

This paper describes the results of the first comprehensive analysis of a range of popular on-line, multiplayer, game servers. The results show that the traffic behavior of these servers is highly predictable and can be attributed to the fact that current game designs target the saturation of the narrowest, last-mile link. Specifically, in order to maximize the interactivity of the game itself and to provide relatively uniform experiences between players playing over different network speeds, on-line games typically fix their usage requirements in such a way as to saturate the network link of their lowest speed players. While the traffic observed is highly predictable, the traces also indicate that these on-line games provide significant challenges to current network infrastructure. As a result of synchronous game logic requiring an extreme amount of interactivity, a close look at the trace reveals the presence of large, highly periodic, bursts of small packets. With such stringent demands on interactivity, routers must be designed with enough capacity to quickly route such bursts without delay.

In this paper we explore the use of priority progress streaming (PPS) for video surveillance applications. PPS is an adaptive streaming technique for the delivery of continuous media over variable bit-rate channels. It is based on the simple idea of reordering media components within a time window into priority order before transmission. The main concern when using PPS for live video streaming is the time delay introduced by reordering. In this paper we describe how PPS can be extended to support live streaming and show that the delay inherent in the approach can be tuned to satisfy a wide range of latency constraints while supporting fine-grain adaptation.

A network running OSPF takes several tens of seconds to recover from a failure, using the current default parameter settings. The main component of this delay is the time required to detect a failure using the hello protocol. Reducing the value of the hellointerval can speed up the failure detection time. However, too small a value of the hellointerval can result in an increase in network congestion, potentially causing multiple consecutive hellos to be lost. This can lead to a false breakdown of adjacencies between routers. Such false alarms not only disrupt network traffic by causing unnecessary routing changes, but also increase the processing load on the routers, which may potentially lead to routing instability. In this paper, we investigate the following question - what is the optimal value for the hellointerval that will lead to fast failure detection in the network, while keeping occurrences of false alarms within acceptable limits? We examine the impact of both network congestion and the network topology on the optimal value for the hellointerval. Additionally, we investigate the effectiveness of faster failure detection in achieving fast failure recovery in OSPF networks.

For many on-line games, user experience is impacted significantly by network latency. As on-line games and on-line game servers proliferate, the ability to discover and connect to nearby servers is essential for maintaining user satisfaction. In this paper, we present a redirection service for on-line games based on the geographic location of players relative to servers. As our results show, the service better meets client demand, saving each client and the Internet as a whole, thousands of miles of networking inefficiency.

This demonstration will show the video sensor networking technologies developed at the OGI School of Science and Engineering. The general purpose video sensors allow programmers to create application-specific filtering, power management, and event triggering mechanisms. The demo will show a handful of video sensors operating under a variety of conditions including intermittent network connectivity as one might see in an environmental observation application.

Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This paper describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe a video sensor platform that can deliver high-quality video over 802.11 networks with a power requirement of approximately 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive long-periods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system.

This paper presents a general design strategy for streaming media applications in best effort computing and networking environments. Our target application is video on demand using personal computers and the Internet. In this scenario, where resource reservations and admission control mechanisms are not generally available, effective streaming must be able to adapt in a responsive and graceful manner. The design strategy we propose is based on a single simple idea, priority data dropping, or priority drop for short. We evaluate the efficacy of priority drop as an adaptation tool in the video and networking domains. Our technical contribution with respect to video is to show how to express adaptation policies and how to do priority-mapping, an automatic translation from adaptation policies to priority assignments on the basic units of video. For the networking domain, we present priority-progress streaming, a real-time best-effort streaming protocol. We have implemented and released a prototype video streaming system that incorporates priority-drop video, priority mapping, and priority-progress streaming. Our system demonstrates a simple encode once, stream anywhere model where a single video source can be streamed across a wide range of network bandwidths, on networks saturated with competing traffic, all the while maintaining real-time performance and gracefully adapting quality.

This paper describes the results of a 500 million packet trace of a popular on-line, multi-player, game server. The results show that the traffic behavior of this heavily loaded game server is highly predictable and can be attributed to the fact that current game designs target the saturation of the narrowest, last-mile link. Specifically, in order to maximize the interactivity of the game and to provide relatively uniform experiences between all players, on-line games typically fix their usage requirements in such a way as to saturate the network link of their lowest speed players. While the traffic observed is highly predictable, the trace also indicates that these on-line games provide significant challenges to current network infrastructure. Due to synchronous game logic requiring an extreme amount of interactivity, a close look at the trace reveals the presence of large, highly periodic, bursts of small packets. With such stringent demands on interactivity, routers must be designed with enough capadty to quickly route such bursts without delay. As current routers are designed for bulk data transfers with larger packets, a significant, concentrated deployment of online game servers will have the potential for overwhelming current networking equipment.

Building efficient ad hoc networks for wireless communications is challenging due to the dynamic nature of the hosts. Broadcast service in ad hoc networks is critical in supporting various applications and protocols. However, excessive redundant retransmissions of traditional broadcast protocols in mobile wireless ad hoc networks have caused the infamous "broadcast storm problem." Various broadcast protocols have been proposed to alleviate the broadcast storm problem. We identify two primary design issues, namely defer time generation and redundant message classification, for all these protocols. We propose a distance-based defer time scheme for the first issue and an angle-based scheme for the second issue. The two schemes together result in a broadcast protocol that enjoys flooding's high reachability and non-flooding schemes' bandwidth efficiency.

In this paper, we describe error preserving encryption mechanisms for transmission of vido over wireless networks. One of the main problems with the secure transmission of data over wireless networks is that the bit errors that occur need to typically be sesolved before decryption can begin. For vido straming applications, this is unacceptable due to the general requirement that video be presented to the user in a continuous manner with low latency. In this paper, we describe a systematic approach to understanding error preserving encryption algorithms. That is, encryption algorithms designed specifically for video to solve this problem. The main objective of this work is to ensure that the basic encryption of the stream can survive bit errors and that the errors are then passed to the application. We make use of the fact that video compression typically results in random byte distribution. Error preserving encryption algorithms are secure against ciphertext only attacks but vulnerable against known plaintext attacks. We limit this vulnerabillity by requiring a key exchange for each session.

We propose two new techniques for the delivery of compressed prerecorded video streams across best-effort networks like the Internet. Current approaches for the delivery of stored video across best-effort networks typically alter the quality of the video frames, the frame rate delivered to the user, or a combination of both. By using network feedback, these algorithms continually adjust the video quality to fit within the available network resources. These approaches, however do not take advantage of the a priori information available from stored video streams, namely the frame sizes that the movie consists of. We show how monitoring the a priori information and actively monitoring a client-side buffer can help smooth the video frame rate delivered to the user, providing a more consistent quality of video.

Intelligent Transportation Systems (ITS) have become a focus for many countries. To achieve ITS, Inter Vehicle Communication (IVC) is required for the exchange and distribution of data such as congestion or emergency information. If this communication can be done without fixed infrastructure, the systems can be deployed quickly and on a larger scale. Ad hoc networking technologies are one such technology to achieve IVC. However, if generic ad hoc network solutions are applied directly to IVC, performance can degrade quickly as the system scales particularly for broadcast type messages. In this paper we propose two new broadcast protocols that reduce bandwidth required for broadcast communication by taking advantage of a vehicle's highly directional movement and Global Positioning Information. To show the performance of our new protocols, we compare our approach with generic ad hoc broadcasting techniques. Our results show that it is possible to achieve several hundred percent improvement of bandwidth utilization with very slight sacrifice of reachability.

There is a great demand for wireless devices that allow users to freely roam and still access their on-line information. As wireless networking technologies continue to develop, the access to multimedia data such as video will continue to become more viable. Limited bandwidth is a problem when wireless devices are used for video transmission and limited power necessitates power efficient mechanisms. We propose a single retransmission based forward error correction (FEC) scheme that increases the video quality of the video, has efficient power consumption, and performs better in a highly dynamic wireless networks.

The efficient transmission of constant quality compressed video streams is complicated by the burstiness (both short term and long term) that video compression standards such as MPEG introduce. Most of the techniques in the literature are concentrated on stored video traffic smoothing or real-time video traffic smoothing. However there is a growing number of live video applications, such as videocasts of courses or television news, where many clients may be willing to tolerate a playback delay of several seconds or minutes in exchange for a smaller throughput requirement. Bandwidth smoothing for these live video applications is referred to as online smoothing. In order to measure the effectiveness of online video smoothing methods, in this paper, we first propose a benchmark algorithm which provides the upper bound on some of the smoothness parameters in the smoothing results. Based on this algorithm, we found that significant discrepancy exists between the results produced by the existing online smoothing methods and the upper bound. With this observation, we then focus on devising algorithms which improve the smoothing results.

This paper proposes a new way to incorporate H.263 video into existing voice/data code-division multiple access (CDMA) networks using a multi-code access protocol. A channel allocation policy is proposed that "shares" codes for larger I-frame video transmission and allocates a fixed number of codes for P-frame transmissions. The base station in a given cell of a CDMA network controls this allocation method, but bandwidth access distribution is based on both mobile host requests and base station forced requests. This scheme allows more efficient allocation of resources in the bandwidth-limited wireless spectrum and improves the quality of service (QoS) of video traffic over this lossy environment.

Multimedia applications that transmit long continuous streams of data across the Internet will continue to stress networking technologies for the years to come. To help improve the performance of congestion avoidance protocols like TCP and to punish non-adaptive UDP-based applications, the IETF is considering the widespread deployment of random early detection (RED) queue management. We examine the impact of RED queue management on multimedia applications and their congestion control mechanisms. Our results show that even in ideal scenarios, the use of TCP-based congestion control in conjunction with active queue management based on RED will add significantly more bandwidth jitter over both short and long time scales, making it particularly difficult for adaptive, TCP-compatible, multimedia applications to converge on a single, fixed sending rate.

Article Proactive buffer management for the streamed delivery of stored video Share on Authors: Wu-chi Feng The Ohio State University, Department of Computer and Information Science, Columbus, OH The Ohio State University, Department of Computer and Information Science, Columbus, OHView Profile , Brijesh Krishnaswami The Ohio State University, Department of Computer and Information Science, Columbus, OH The Ohio State University, Department of Computer and Information Science, Columbus, OHView Profile , Arivind Prabhudev The Ohio State University, Department of Computer and Information Science, Columbus, OH The Ohio State University, Department of Computer and Information Science, Columbus, OHView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 285–290https://doi.org/10.1145/290747.290783Online:01 September 1998Publication History 11citation427DownloadsMetricsTotal Citations11Total Downloads427Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The transfer of prerecorded, compressed video requires multimedia services to support large fluctuations in bandwidth requirements on multiple time scales. Bandwidth smoothing techniques can reduce the burstiness of a variable-bit-rate stream by prefetching data at a series of fixed rates, simplifying the allocation of resources in video servers and the communication network. Given a fixed client-side prefetch buffer several bandwidth smoothing algorithms have been introduced that are provably optimal under certain constraints. This paper presents a collection of metrics for comparing these smoothing algorithms and evaluating their cost-performance trade-offs. Due to the scarcity of available trace data, we have constructed a video capture testbed and generated a collection of twenty full-length, motion-JPEG encoded video clips. Using these video traces and a range of client buffer sizes, we investigate the interplay between the performance metrics through simulation experiments. The results highlight the unique strengths and weaknesses of each algorithm.