real-time video encoder

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Multicore based 3D DWT video encoder

Multicore based 3D DWT video encoder

Currently, most of the popular video compression technologies operate in both intra and inter coding modes. Intra mode compression operates in a frame-by- frame basis while inter mode achieves compression by applying motion estimation and compensation between frames and taking advantage of the temporal correla- tion between frames. Inter mode compression is able to achieve increased coding efficiency over intra mode schemes. However, in video content production stages, digital video-processing applications require fast-frame random access to perform an undefined number of real-time decompressing-editing-compressing interactive operations, without a significant loss of original video content quality. Intra-frame coding is desirable as well in many other applications like video archiving, high-quality high-resolution medical and satellite video sequences, applications requiring simple real-time encoding like video-conference systems or even for professional or home video surveillance systems [1], and digital video recording systems, where the user equipment is usually not as powerful as the head end equipment.
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An Efficient and Secure Video Encryption Technique for Real Time Systems

An Efficient and Secure Video Encryption Technique for Real Time Systems

It is a straight forward mpeg video encryption which incorporates encryption along with MPEG compression within a single step [12]. The main aim of this method is to reduce the computation time by taking the advantage of simulating MPEG compression as well as data encryption simultaneously at the same time and also avoid decreasing video compression rate. Huffman word list is used as a secret key in this permutation. During MPEG encoding, the encoder uses the secret key rather than standard Huffman word list. The usage of Huffman word list to encode the MPEG video reduces the compression rate as compression rate is highly depends on the Huffman codeword list.
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Chapter 1 Knowing Your Video Encoder Chapter 2 Hardware Installation Chapter 3 Accessing the Video Encoder Chapter 4 Configuring the Video Encoder

Chapter 1 Knowing Your Video Encoder Chapter 2 Hardware Installation Chapter 3 Accessing the Video Encoder Chapter 4 Configuring the Video Encoder

Compared to the conventional camera, this Video Encoder features a built-in CPU and web-based solutions that can provide a cost-effective solution to transmit the real-time high-quality video images and sounds synchronously for monitoring. The Video Encoder can be managed remotely, so that you can use a web browser to access and control it from any desktop/notebook computer over the Intranet or Internet. TV-VS1P Compliant with IEEE802.3af PoE (Power over Ethernet) standard, the Video Encoder provides you with more flexibility of device
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Digitizer Capture Card - PCI RGB User Manual

Digitizer Capture Card - PCI RGB User Manual

The Digitizer Capture Card is a PCI plug in card, and is compatible with Windows 2000/XP and Linux operating systems. The card is designed to work with software such as Windows Media Encoder for real time streaming and archiving of high resolution/frame rate graphics and video.

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Efficient Energy Based Reliable Protocol for Real Time and Non-Real Time Data Transmission in Multicast Streaming in MANET

Efficient Energy Based Reliable Protocol for Real Time and Non-Real Time Data Transmission in Multicast Streaming in MANET

[1]Kavitha Subramaniam(2016) et al: In Mobile Ad-hoc Networks(MANET) multicast streaming is handled by various buffer management techniques since it involves real-time data. From source to destination, the video data can be buffered in all the intermediate nodes. Buffer management protocols are used to manage and streaming of data in multicast group. After receiving the packets at destination, they are divided into two groups: real-time and non-real time and then are placed in queues respectively. Cumulative weights of the packets in real-time buffer are calculated and then transmission priorities are assigned. The buffer space is adapted according to the number of nodes present in source and destination. This buffer management protocol increases the packet delivery ratio and reduces the latency and energy consumption.
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Services for gaming-on-demand

Services for gaming-on-demand

Additional challenges occur in a complex network setting, where the game server is very distant from the user terminals, and the video stream crosses several administrative network domains. The goal of an end-to-end system is to grant service providers means to orchestrate efficiently the different components of an uninterrupted service: networking, system and application components. This is particularly important when adapting the audio-visual services to new technologies, or new administrative and market situations. Co-ordination of various elements spanning customer premises equipment, access, aggregation and transport networks, and content distribution/video processing tools should be achieved for smooth service operation. Its importance is particularly amplified in closed – complex private network – settings for video-service distribution (Figure 3). Integrity of the service should be preserved.
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Design and Analysis Scalable and Interactive Video-On-Demand System

Design and Analysis Scalable and Interactive Video-On-Demand System

The implementation of a video network streaming system implementation on a raspberry pi board. The live streaming video content from the single server to multiple clients. The stored video delivered to the single server through to the multiple clients, using RTSP protocol. The test results prove that it completely satisfies the embedded system user’s demand and performs well [1]. The raspberry pi of capabilities over arduino and advantage of the pibot more than the predictable observation system. Also by mean it the ability to detect and recognize faces it be able to finished to on the alert us about every unknown person and obtain a snap of it and email us the same. Live video streaming application survey on arm 11 board [3]. The surveillance system uses H.264 video coding standard to encode the video data in order to lower the bit rate to improve the network adaptability. The surveillance system there excellent video quality and give of the network condition [4].
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Video Communication for Using of Telemedicine in Traffic Accidents

Video Communication for Using of Telemedicine in Traffic Accidents

Telemonitoring is an active process and comprises the ability to guide, direct, and interact with another health care professional (in this case, a surgeon) in a different location during an operation or clinical episode. The level of interaction from the mentor can be as simple as verbal guidance while watching a transmitted real-time video of the operation (Challacombe, 2010). Surgery is, most of all, a visual specialty. Live pictures provide detailed information about anatomic landmarks, giving the mentor instant information about the patient’s normal anatomy and pathological structures. Based on this instant information, the mentor can give advice to the operating surgeon and immediately correct his or her surgical actions (Augestad & Lindestmo, 2009). Telementoring requires a secure high- speed connection with sufficient bandwidth to transmit a good picture and audio quality to the mentor’s station. It has been shown that surgeons are generally able to compensate for delays of up to 700 ms, but delays over 500 ms are quite noticeable (Fabrizio et al., 2000). If using an ISDN connection, a bandwidth of 384 kB per second is needed to give sufficient picture quality for accurate interpretation by the mentor, although clinical work has been carried out using bandwidths as low as 128 Kb per second (Rosser et al., 1999). There is a knowledge gap between central and local hospitals, which is even more problematic in mainly rural countries, with community surgeons dispersed in remote corners of a large country (Anvari, 2007). The introduction of VC as an educational tool has led to a decrease in this knowledge gap. Until recently, the only proven technique for teaching surgeons new skills was one-site mentoring completed with hands-on course training
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Real Time Video Processing For Drone-Based Lightning Sensor

Real Time Video Processing For Drone-Based Lightning Sensor

I also would like to express my sincere gratitude to my supervisor Dr. Mohd. Riduan bin Ahmad for the continuous support for my Bachelor Degree study and research, for her patience, motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better supervisor for my Bachelor Degree study

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Sensemaking: A Proposal for a Real Time on the Fly Video Streaming Platform

Sensemaking: A Proposal for a Real Time on the Fly Video Streaming Platform

Video streaming has become an important element for contemporary research because it allows that an activity can be shared, discussed and evaluated by several researchers at the same and in real-time (Lima, 2012; Yoshitaka & Deguchi, 2009; de Almeida et al., 2014). However, the streaming not only creates an intense volume of data but also ge- nerates the need to increase hardware resources for storing the recordings, and also the streaming generates extensive “raw” materials without any viewable narrative. The real-time transmission of moving images with ultra-high resolutions (4K, 8K) results in excess of images and data that make storage and retrieval of data (content) stored very complex and expensive (de Almeida et al., 2011; Weekley & de Laat, 2016; Liu et al., 2011). Usually, a significant amount of videos are daily generated for research and teaching activities, such as surgeries, advanced visualizations, lectures and experiments records and the edition of a raw video demands hours, often invalidating its use due to the time spent on viewing or researching a desired content. The goal of this article is to demonstrate the development of a video editor that can edit on the fly videos that are being streamed live in real-time. To our knowledge, this is the first known live stream- ing video editor.
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Real-Time Monitoring of Video Quality in IP Networks

Real-Time Monitoring of Video Quality in IP Networks

The goal of a comprehensive framework for estimating video quality over packet networks was shared by several other works. In particular, the framework of Reibman et al. [12], [13] is closest to ours in terms of motivation and approach. In fact, our initial model can be viewed as belonging to its NoParse class of methods [12]. Unlike methods in the Full- Parse and QuickParse classes [12], NoParse methods do not rely on deep packet inspection or explicit parsing of the video bit stream. Hence, they have much lower complexity, at the cost of generating less accurate video quality estimates. Our approach also differs from the existing NoParse method [12] in two aspects. First, the existing method [12] models video quality as a linear function of loss rate, which is less accurate with bursty losses [9], [12]. Our model is designed to account for both loss rate and loss burstiness, and in particular their impact on the effectiveness of loss recovery mechanisms. This significantly improves the accuracy of quality estimates across loss patterns [18], [19], while remaining considerably simpler than the FullParse and QuickParse methods. Second, as pointed out by Reibman et al. [12], the NoParse method requires calibrating the distortion caused by single losses. This calibration is unfortunately dependent on the specific content of the video stream, making it difficult to carry out in real-time. Our loss-distortion model exhibits similar limitations, but we overcome this problem (see Section V) by introducing a new quality metric—rPSNR—that can be estimated independent of video content.
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Real-Time Video Imaging of Protease Expression In Vivo

Real-Time Video Imaging of Protease Expression In Vivo

With the development of hydrophilic near-infrared (NIR) dyes and quenchers, it is now possible to use conventional molecular beacon con- structs as in vivo imaging agents [6-7]. These probes are optically silent (quenched) in their native state and are activated in the presence of a specific protease, thereby generating an NIR fluorescence signal. However, the inherent instability, short half-life, and nonspecific activation of peptides and small com- pounds are still major obstacles to their in vivo appli- cation by systemic administration. Conjugation of macromolecules, such as high molecular weight poly(amino acids) and poly(ethylene) glycol (PEG), efficiently increases the in vivo stability [8], but de- creases the sensitivity and specificity of the probes. This is because conjugated macromolecules require longer circulation times to produce high contrast im- ages through accumulation at tumor sites by the en- hanced permeability and retention effect (EPR), and a consequence can be non-specific signal activation by proteases present in the blood. For example, com- mercially available VisEn’s protease activatable probes targeting matrix metalloproteinases (MMPs) (MMPSense™, VisEn, Bedford, MA, USA) have been widely used [9]. However, the probes typically take a long time (~ 24 hr) to be fully activated in vitro and in vivo, which may be due to their conjugated, high-molecular-weight polymer backbone. Delayed activation hampers real-time and high-throughput in vivo applications. Therefore, it is important to strike a balance between stability and sensitivity of the probes in vivo to enable quick screening and true real-time imaging of enzyme activity in live animals, and to achieve superior target-to-background contrast. Re- cently, we and others have developed various novel activatable imaging probes that can provide high-resolution imaging and low background signals [3, 10-13]. Although these reported systems are sensi- tive, they have limited applications due to the modest and delayed fluorescent changes of the probes, thus
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A video based real time fatigue detection system

A video based real time fatigue detection system

According to our common experience, most facial expressions can be classified properly by human beings from static pictures of faces. This observation has been successfully utilized by Ekman and Friesen to invent their FACS. A reasonable interpretation of how human emotion can be guessed from static images is that a neutral face is always implicitly defaulted in your mind when you watch a picture containing an expressed face. The difference between the expressed face and the neutral face in fact tells the dynamic information which is used implicitly by humans for emotion recognition. Therefore, the real problem of how to handle facial expression events is that the events are dynamic and time-varying. Since a facial expression event consists of sequential facial expressions and individual facial expressions can be specified by action units, the key to characterizing facial expression events is to exploit a temporal combination of action units specifying individual facial expressions. The analysis of facial expression events becomes a problem of how to identify such temporal rules which govern facial expression variation behind expression events. The temporal behavior of expression events can be extracted based on the observation that the measured action units at each frame look apparently random. However, they are fully controlled by invisible, internal states. Therefore, it is natural to employ Hidden Markov Models (HMM) to model and specify facial expression events.
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Performance Analysis of Real and Non-real Time Traffic under MANET Routing Protocols

Performance Analysis of Real and Non-real Time Traffic under MANET Routing Protocols

The average throughput of the routing protocols is analyzed as the second metric. The Figures 4 and 5 show the average throughput of the AODV, DSR, OLSR and GRP protocols for non-real and real time traffic respectively. For the non-real time traffic shown in Figure 4, it is obvious that the OLSR protocol overtakes other three routing protocols by achieving the highest throughput. The higher performance achieved by the OLSR protocol is due to the proactive characteristics of this protocol. It constantly sets up, maintains and updates the routing information with the assist of MPR in the network, which leads to the reduction of routing overhead [14]. In order to maintain good network performance, congestion control mechanism must be provided to prevent the network from being congested for any significant period of time.
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Making Telehealth Sustainable in South Australia Dr Victoria Wade

Making Telehealth Sustainable in South Australia Dr Victoria Wade

analyses of telehealth services using real time video communication. Impact of[r]

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FB Managed Service Practical Solutions

FB Managed Service Practical Solutions

  Store & Forward Video: FTP of large, compressed video files of High resolution, High quality.   Video Streaming: Real-time Surveillance, Tele-[r]

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Embedded Real Time Video Monitoring System using Arm

Embedded Real Time Video Monitoring System using Arm

Abstract: - In this paper, Embedded Real-time video monitoring system based on ARM is designed, in which the embedded chip and the programming techniques are adopted. The central monitor which adopts S3C6410 chip as controller is the core of the whole system. First, USB camera video data are collected by the embedded Linux system, processed, compressed and transferred by the processing chip. Then, video data are sent to the monitor client by wireless network. Tests show the presented wireless video surveillance system is reliable and stable. And it has a perfect application prospects with real-time monitor.
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A Self-adaptive and Real-time Panoramic Video Mosaicing System

A Self-adaptive and Real-time Panoramic Video Mosaicing System

Difference from traditional video surveillance system , panoramic video surveillance system can provide viewers with a complete 360o view. MA Li,ZHANG Mao-jun , XU Wei, etal.[8]designed KD-PVS , one embedded high resolution panoramic video surveillance system . They introduces the multiple-camera configuration and video mosaicing algorithm to stitch the video data from multiple camera sources into the panoramic video. KD-PVS system is very convenient for various situations , warehouses , prisons , mobile monitoring , etc , especially useful for indoor monitoring . ZHAO Hui and others [9]presented an improved fully-automatic image mosaic algorithm is.By sorting the unordered image sequence and roughly computing the translation offset between adjacent images to speeds up corner match procedure and improves matching stability ,and then using RANSAC algorithm to eliminate outliers to ensure effectiveness of the matched corner pairs , finally using a multi-band blending technique to generate the final panorama . It has less blur or ghost effect after blending , especially when there are noise , moving objects , repeated texture and small overlaps presented in the images .
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VLSI implementation of  modified guided filter for real time video 

VLSI implementation of  modified guided filter for real time video 

Real time image and video processing are used in a wide variety of application from video surveillances and traffic management to medical image application. Many new and exciting innovation such as HDTV and digital cinema involved image and video processing. Standard NTSC video are digitalized at the frame rate of 720x 480.NTSC color encoding used with the system M television signal consists of 29.97 interlaced frames of video per seconds. Each frame is composed of two field consisting 262.5 scan lines for a total scan line of 525 and 483 scan lines are make a visible data. Vertical blocking interval allow the vertical synchronization and retrace. NTSC field refresh in the black and white color originally extract the matched nominal color of 60Hz frequency replacement the power. NTSC use a luminance- chrominance encoding system. The three color picture signals are divided into RGB (Red, green, blue).Chrominance signal are carry only the color information.
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Real Time Video Surveillance Architecture for Secured City Automation

Real Time Video Surveillance Architecture for Secured City Automation

around the globe. The project presents a multilevel surveillance security system, which does not require human- to-human or human-to-computer interaction. The project aims at using Raspberry pi which controls motion detectors and video cameras for remote sensing and surveillance, streams live video and records it for future playback. The purpose of the project is to provide security automation in the city via camera based object tracking, motion detection when an intrusion happens along with pollution detection monitoring system. This system also tracks any object in real time meaning we will be completely aware of precise location of the object to see its movement in real time. Raspberry pi a single board computer that carries motion detection algorithm with python as default programming environment. A camera module connected to the Raspberry Pi will record all the happenings in the monitored area and live streaming can be viewed from any web browser, even from mobile in real- time. To use the storage efficiently, we apply the Motion Detection algorithm. In this algorithm the media is recorded and stored on a local disk only when motion is recognized, the motion detection is achieved by using IR sensor. In normal scenario, for a video of 5seconds, it requires 11MB of storage whereas for motion detected media a reduced storage of 3MB was achieved. Monitoring the surrounding and providing an alert via GSM module in case of pollution and fire accidents. In object tracking we feed the data set for the objects we wish to track, the program extracts the features of the captured image. When the features match with the given data set a luminous highlighting helps to track the path of the object within the camera coverage.
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