Research and Implementation of Multimedia Server Management in Distributed Multimedia Distance Education System

Real-time video streaming transmission has been widely used in security monitoring field. However, audio function in the monitoring system has not attracted significant attention. As a well-known streaming media server in security monitoring field, Live555 projects have realized the real-time video capture and transmission with the secondary development, but they still lack the collection and transmission of real-time audio on IP camera. In this paper, we propose a streaming media transmission scheme to realize simultaneous transmission of real-time audio and video based on the Live555 project. In order to realize the above scheme, we add the real-time audio collection module, rewrite the related classes and methods in the project to transport real-time audio data with video. It is demonstrated by experiments that the real-time audio and video simultaneous transmission is stable, the delay is low and the quality is good. It can be played on mainstream players such as VLC and FFplay.

A multimedia telemedicine system (MTS) using Transfer Control Protocol and Internet Protocol (TCP/IP) over the Internet is developed. Doctor with patient and doctor can communicate each other using this system. Real-time data, including audio, video and instant message (IM), and non-real-time data, including vital sign signals, radiological images with DICOM 3.0, file, bio-signal, bio-data and so on, can be exchanged on the system. This system's architecture is client/server mode. All data are encoded/compressed before transferring through Internet/Intranet. The real-time audio is encoded and decoded by MPEG (Moving Picture Experts Group) audio layer 3 algorithm and real-time video is encoded and decoded by MPEG-4. The software implementation of needed functionality without any externally attached hardware CODEC (Coder/Decoder) units enables the compact design with low cost. The real-time video has 25 frames per second at Local Area Network (LAN) and more than 20 frames per second at ADSL.

This thesis provides a detailed analysis of the effects of TCP and UDP traffic over a LAN and WAN medium. In addition, it also analyses some real time applications like audio, video and web browsing that is affected by TCP traffic while sharing a bottleneck node and/or link resources. As network industry is growing continuously, the network administrator should be aware of TCP and UDP traffic that is traversing through their network. The analysis and monitoring of the traffic is crucial as it directly affects the performance of the network. Finding a cause for the poor performance of the network is quite important because it gives an idea to troubleshoot and resolve the issues effectively. In this thesis, we have created topologies using Cisco’s Virtual Internet Routing Lab (VIRL) [7]. They replicate an organizational infrastructure with client-server environment in LAN and WAN. Routing protocols such as OSPF and BGP are used to mimic the real world internet. A built-in LXC-iperf tool is used to generate the TCP and UDP traffic. During the generation of the traffic, various parameters are changed or controlled to see their effect on the network performance. As a learning and informative research, this thesis considers several Quality of Service (QoS) parameters that characterize the performance of an overall network. In particular, this thesis obtains packet loss, throughput, and jitter as QoS parameters when the resource has both TCP and UDP traffics simultaneously. We have examined, the effects on (i) TCP throughput and packet loss and (ii) UPD packet loss and jitter of (a) real time audio, (b) video, and (c) web browsing applications. These parameters examine how the traffic be manipulated to keep minimum packet loss, minimum jitter and maximum throughput. It is needless to say that all are competing with each other (TCP/UDP traffic) for sharing bottleneck resources. We have used a sample time of 15 seconds for each of our experimental results presented in this thesis. Our analysis shows that the best performance of the real time video and audio application is obtained when we select large size packet but its size being less than MTU of the link (without reducing the data rates). Similarly, in case of web browsing, we notice that throughput increases by increasing the window size and decreasing the latency. Efficient outcomes with the traffic analysis are achieved only if the experiments are carried out with adequate amount of attention.

In order to achieve two-way real-time audio and video transmission in low bandwidth satellite communication channel between the helicopter and the ground station, we propose a real-time audio and video transmission design and implementation to control transmission delay and bit error rate in a reasonable range. This particular method really ensures the full use of the satellite channel and transmission quality of audio and video, at the same time, reduces the transmission delay and smooth video shows, etc. By simulation experiments, some satisfactory results in transmitting have been achieved.

DOORAE (Distance Object Oriented Collaboration Environment) is a framework for supporting development on multimedia collaborative environment. It provides functions well capable of developing multimedia distance education system for students as well as teachers. It includes session management, access control, concurrency control and handling late comers. There are two approaches to software architecture on which applications for multimedia distance education environment in situation-aware middleware are based. This paper proposes a new model of fault tolerance agent based on situation-aware ubiquitous computing for a multimedia home study system which is based on CARV.

In this paper authors have proposed a VAS for Personal Video Recorder (PVR) enabled iSTB. The proposed solution can recognize the regions in the frames where the recipe and ingredients are shown and can save them in image/text format for further use. We have used a hybrid approach where the text regions are localized using the compress domain features of the streaming video in real time and then some pixel domain processing is performed on that reduced region of interest to recognize the text. The novelty of the proposed approach lies in using the compressed domain features of H.264 video in text localization and applying some pixel domain post processing on the ROI to perform the entire recognition process in real time. The system is tested over a video upto CIF resolution and can recognize the text successfully with an accuracy of nearly 80%.

In this paper, a proposed system for automatic counting and visual multi-tracking for human sperm in microscopic video frames is presented. It can be easily turned into a commercial computer-assisted sperm analysis (CASA) system. CASA systems help in detecting infertility in human sperm according to clinical parameters. The proposed system consists of nine phases and it counts sperm in every single frame of video in real time and calculates the average sperm count through the whole video with accuracy 94.3% if it is compared to the manual counting. Also, it tracks all identified sperm in video frames in real time. It works with different frame rates above 15 frame/s to track visually the movements of the sperm. The dataset consists of three high-quality 1080p videos with different frame rates and durations. Finally, the open challenging research points are addressed.

In this paper we present Palpatine, an IMS application server providing a point-to-multi-point video share delivery service where a video share capable 3G device can capture audio and video in real time and send it lively to a group of 3G terminals wishing to receive it. Palpatine integrates seamlessly into IMS infrastructures without requiring any network modification and fulfilling all required standards. For this reason, Palpatine can be directly used over any IMS-compliant network without requiring the installation of any external application on current commercial video share capable mobile devices.

In this paper, we propose hardware architecture for a high-speed context-adaptive variable length coding (CAVLC) decoder in H.264. In the CAVLC decoder, the codeword length of the current decoding block is used to determine the next input bitstreams (valid bits). Since the computation of valid bits increases the total processing time of CAVLC, we propose two techniques to reduce processing time: one is to reduce the number of decoding steps by introducing a lookup table, and the other is to reduce cycles for calculating the valid bits. The proposed CAVLC decoder can decode 1920×1088 30 fps video in real time at a 30.8 MHz clock.

This paper presents an efficient motion compensation architecture for MPEG-4 advanced simple profile at level 5 (ASP@L5) streaming video standard. A novel local memory storage scheme for motion vector prediction is proposed in the paper. In this scheme, local memory for storing differential data of motion information in video packet layer is avoided, and total memory access bandwidth is reduced 1/3. The motion compensation unit is implemented using 0.25mm 1-poly 5-metal CMOS technology, which occupies a silicon area of 1.35t1.35 mm2 and operates at 150MHz. Simulation result shows that it is capable of compensating AS profile sequences at ITU-601 resolution video in real time.

Image-guided therapy is the treatment technology which uses medical images to understand anatomic information on human body and make medical treatment plan. Such treatment includes radio frequency (RF) ablation, high intensity focused ultrasound (HIFU), cyber knife, surgical robot, etc. Especially, ultrasound (US) guided HIFU therapy has attracted much attention since it treats internal organs without giving any radiation dose to human body. However, it is still challengeable to track 3D position of tumors of moving organ from 2D us videos since respiration makes change on the position and shape of the internal organs such as liver, kidney or pancreas. The paper proposes a moving organ tracking method based on 3D organ model. The 3D organ model is made from MR or CT images captured before treatment. The 3D organ model is registered to US images, and the organ and its tumor's position are tracked in 2D US videos in real time. We apply the method on a liver phantom and get 5.3ms as the processing time and 1.3 mm as the accuracy.

Efficient pattern recognition in motion imagery has become a growing challenge as the number of video sources proliferates worldwide. Historically, automated analysis of motion imagery, such as object detection, classification and tracking, has been accomplished using hand-designed feature detectors. Though useful, these feature detectors are not easily extended to new data sets or new target categories since they are often task specific, and typically require substantial effort to design. Rather than hand-designing filters, recent advances in the field of image processing have resulted in a theoretical framework of sparse, hierarchical, learned representations that can describe video data of natural scenes at many spatial and temporal scales and many levels of object complexity. These sparse, hierarchical models learn the information content of imagery and video from the data itself and lead to state-of-the-art performance and more efficient processing. Processing efficiency is important as it allows scaling up of research to work with dataset sizes and numbers of categories approaching real-world conditions. We now describe recent work at Los Alamos National Laboratory developing hierarchical sparse learning computer vision models that can process high definition color video in real time. We present preliminary results extending our prior work on object classification in still imagery [1] to discovery of useful features at different time scales in motion imagery for detection, classification and tracking of objects.

Video surveillance cameras are placed in many places such as bank, hospital, toll gates, airports, etc. To take advantage of the video in real time, a human must monitor the system continuously in order to alert security officers if there is an emergency. Besides, for event detection a person can observe four cameras with good accuracy at a time. Therefore, this requires expensive human resources for real time video surveillance using current technology. The trajectory of one or more targets obtains for object tracking while recording above space and time. By tracking various objects, the burden of detection by human sentinels is greatly alleviated. Efficient and reliable automatic alarm system is useful for many ATM surveillance applications. ATM Video monitoring systems present many challenging research issues in human abnormal behaviors detection approaches. The framework of ATM video surveillance system encompassing various factors, such as image acquisition, background estimation, background subtraction, segmentation, people counting and tracking are briefly discussed in this chapter.

This paper presents an efficient architecture for Motion Estimation (ME) based on the Diamond Search (DS) Algorithm. This architecture also includes the Motion Compensation (MC) for luminance samples, reusing internal ME results and avoiding the additional external memory accesses for the MC operation. The proposed architecture also generates inputs for a Fractional Motion Estimation (FME) for quarter sample precision, as proposed by the H.264/AVC standard. The main goal of this work is to design a low cost ME architecture for the DS algorithm with reduced number of external memory accesses and ready to be integrated with a fractional interpolator. The designed architecture was synthesized to Altera Stratix 4 family of FPGAs and in the worst case scenario, this architecture is able to process Full HD (1080p) videos in real time.

The novel motion compensation method, called LIC (Local Intensity Compensation), approximates a current block using a linear combination of reference blocks. To reduce the bitrate of high quality images, sparse coefficients are preferable; then, a method applying sparse representation to the LIC has been proposed. A solver of sparse representation called AIHT (Accelerated Iterative Hard Thresholding) uses many vector and matrix operations, and video coding standards encode frames in GOP (Group Of Pictures) structure. In some cases, video coding methods have to encode videos in real time, but the LIC using sparse representation takes a large amount of computational time for the prediction; thus, this paper demonstrates a method to accelerate the LIC using sparse representation as follows: (i) acceleration of the AIHT itself using GPU (Graphics Processing Unit); and (ii) employing parallel processing in multi-core CPU, namely OpenMP, for the independent structure of the GOP. By using OpenMP, multiple GPUs work under different cores. Experimental results show that the methods (i) and (ii) are faster than CPU implementation by about 4 times and about 7.4 times maximum, respectively.