SIViP (2010) 4:263–272DOI 10.1007/s11760-009-0116-5

ORIGINAL PAPER

Content-based network resource allocation for real time remotelaboratory applications

Ankush Mittal · Amit Pande · Praveen Kumar

Received: 2 January 2008 / Revised: 28 January 2009 / Accepted: 29 January 2009 / Published online: 6 May 2009© Springer-Verlag London Limited 2009

Abstract This paper presents a practical solution to makeremote laboratories a realizable dream. A remote laboratoryis an online laboratory where students can get first-hand expe-rience of engineering labs via Internet. Video transmissioncan provide hands on experience to the user but the trans-mission channel or networks typically have variable and lowbandwidth that poses a tough constraint for such implementa-tion. This work presents a practical solution to such problemsby adaptively transmitting the best available quality of labo-ratory videos to the user depending on network bandwidth.The concept behind our work is that not all objects or framesof the video have equal importance, and thus bandwidthreduction can be accomplished by intelligently transmittingimportant parts at relatively higher resolution. A localizedTime adaptive mean of Gaussian (L-TAMOG) approach isused to search for moving objects which are then allocatednetwork resources dynamically according to the varying net-work bandwidth variations. Adaptive motion compensatedwavelet-based encoding is used to achieve scalability andhigh compression. The proposed system tracks the networkbandwidth and delivers optimally the most important con-tents of video to the student. Experimental results over sev-eral remote laboratory sequences show the efficiency of theproposed framework.

A. Mittal · P. KumarDepartment of Electronics and Computer Engineering,Indian Institute of Technology Roorkee,Uttarakhand 247667, Indiae-mail: ankumfec@iitr.ernet.in

P. Kumare-mail: praveen.kverma@gmail.com

A. Pande (B)Department of Electrical and Computer Engineering,Iowa State University, 2215 Coover Hall, Ames, IA 50011, USAe-mail: amit@iastate.edu

Keywords Multimedia systems · L-TAMOG-based imagesegmentation · Video transmission · Interactive lab · Scalablecoding · Adaptive network resource allocation

1 Introduction

Multimedia streaming requires high network bandwidth.While, in many cases, the user may have specific prefer-ences, the multimedia coding schemes do not in generalallow for content-based compression and transmission. Ina remote laboratory one can access and work on instrumentsand experimental setup over the Internet. The earliest experi-ments included robot control and circuit fundamentals[1–4]. The benefits from remote-access laboratory experi-ments for potential users include improved instruction, col-laborative educational programs with other universities,enabler for distance education programs, reduced costs andimproved access for the disabled [1]. However, multimediatransmission requires high network bandwidth.

Various universities around the world have startedE-Learning courses in one way or the other. The course con-tent varies from only lecture slides and laboratory sheets tofull video lectures. A few examples are MIT OpenCourse-Ware [5,6], Berkeley [7] and NPTEL [8] from Indian Insti-tutes of Technology and Indian Institute of Science (IITs andIISc). However, none of these E-Learning systems has pro-vision for any kind of laboratories. In several laboratories,the assistance required from the teacher demands more thanthe teacher’s verbal directions.

The majority of remote laboratories use text-based inter-face for entering input parameters and therefore are a poorreplacement for real laboratories. Even GUIs [9,10] with-out real time videos (RTVs) are poor solutions since thestudent does not get a hands-on experience of real set up.

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Fig. 1 Sample control laboratory video sequence

Thus, streaming videos are necessary so that the remote usergets the sense of “tele-presence” in the laboratory. A recentwork by Kikuchi et al. [11] emphasizes the need of videotransmission in remote laboratories and presents an initialframework. However, their system requires a network band-width of 15 Mbps for efficient remote laboratory experience.Such high bandwidth is generally not possible for majorityof E-learning users. Even the theoretically available band-width over Broadband connection is 256 and 56 Kbps forDial up connections which are most commonly used by per-sonal users. We address the challenge in presenting remotelaboratories videos to the user at such low bandwidth with ournetwork adaptive coding and transmission algorithm whichis based on content-based network resource allocation [12].

This algorithm introduced in this paper is based on novelcontent based compression approach for real time adaptivevideo compression of laboratory video sequences and theireffective transmission to distant user over a possibly low andvarying bandwidth network. In addition to compression, weperform content-based dynamic allocation of network band-width for video transmission to efficiently transmit region ofinterest. The results over control laboratory experiments’ vid-eos illustrate the efficiency of our system are quite promising.

The contributions of our approach can be summarized asfollows. This paper applies the virtues of content-based mul-timedia compression to achieve a network-based allocationscheme to provide the users with best possible multimediaquality. Thus, it delivers real time laboratory videos to theuser with high perceptual quality. We use a localized seg-mentation approach to achieve localized segmentation. Thiswork present a work that works on conducted experiments.

Section 2 provides a brief overview of proposed systemarchitecture. Sections 3 explains the working of the variousmodules especially the RTV streaming. Section 4 gives thedetails of our Network Bandwidth Distribution scheme andexplains it’s working. Results are illustrated in Sect. 4. Futurework and conclusion are presented in Sect. 5.

2 System architecture

The designed remote laboratory architecture gives an interac-tive interface for user and also a practical adaptive backbone

framework for RTV streaming and controls adjustments. Thecircuit analysis and interfacing modules are as typical anddesigned in standard manner [9,10,12] and we will not dis-cuss them in this paper in consideration of space. Our pri-marily focus is on RTV streaming over a low and varyingbandwidth network so that the best content reception is avail-able to the student. The following two subsections providean overview of the outer shell and knowledge of differentblocks of the system. Section 2.1 discusses the user interfacewhile Sect. 2.2 discusses the laboratory setup and backboneframework. The details of other features enabled for real-timedynamic video processing and other architectural modulesare discussed subsequently. The remote laboratory applica-tion is presently experimented for a control laboratory setup.Various experiments such as position control, magnetic lev-itation, etc. are considered for RTV streaming (see Fig. 1).

2.1 User interface

The user interface is illustrated in Fig. 2. The user has thefacility to see the laboratory video at his personal computer.Various controls are provided to the user including the labo-ratory controls such as controlling the values of Proportional,Integral and Derivative controllers if used in the experiment,controlling the oscilloscope controls such as x- and y-axisposition, scale, etc. if oscilloscope is used; reference signalused, etc.

Further advanced inputs are the inputs such as cameraposition control and synchronization and controlling thedesirable maximum output video quality parameters. Thevideo quality parameters that can be controlled include videoresolution, frame rate, average bit per pixel, notify changes,etc. The “Notify changes option” is incorporated to notify theuser only of unpredictable motion and to pop this informa-tion at his desktop in case of such event. This has discussedin Sect. 3.

The system has two separate connections: the first onebeing the connection oriented TCP connection. It is usedfor sending crucial laboratory information and other controldata. The control data for remote laboratories include pre-cise inputs for oscilloscope or other digital control knobsin the distant laboratory. The second socket runs UDP-based

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Fig. 2 Interactive user interface

connection-less protocol for fast RTV transmission. The pro-cessing is handled by the client’s desktop processing unit(CDPU) usually the microprocessor of the available PC. Theprocessors presently available can handle large computa-tions; hence computational part is transferred to client sideto reduce network and server overload. Thus, the processingunit (CDPU) performs the following operations:

• It sends acknowledgements (ACK) or piggybacks ACKover the control information transmitted over to the lab.ACK is required to complete the feedback loop for net-work bandwidth estimation. When no control informationis required to be sent, the CDPU sends a separate ACKpacket.

• When control information is being transmitted, it piggy-backs the ACK into the acknowledgement field of trans-mitted packet.

The Video is transmitted in our scheme as a combinationof several visual blocks (VBs). These VBs are extracted usingthe localized Time adaptive mean of Gaussian (L-TAMOG)classification scheme discussed in next section. The inte-gration and synchronization of different VBs, backgroundand audio stream is another task for CDPU. It also performs

smoothening of reconstructed frames Video integration anddecoding is an important function of CDPU.

The various inputs are plotted against each other at user’sdiscretion to observe the performance of system. For exam-ple it may plot the step response of the given system usingthe inputs and outputs. The plotting is done at client sideto reduce network traffic and also to reduce the overheadfor server which may be required to run several laboratoryexperiments for different users.

CDPU also establishes a text-based chat connectionbetween the teacher and student if the teacher is available.We have considered that teachers may be present in the lab.We provide a text-based chat facility with the teacher to thestudent to clear the doubt. We also incorporate the provisionof transmitting graph plots back to the teacher, if required.

Thus, the CDPU at client side provides an efficient frame-work to provide the user with an interactive interface.

2.2 Laboratory architecture

The laboratory implemented is a control laboratory and wehave worked on streaming of control laboratory videosequences such as speed control, position control, etc. Thelaboratory architecture is illustrated in Fig. 3.

The TCP-based connection oriented connection is reliableand the information on control knobs, camera controls, userpreferences, etc. is transmitted over it. The UDP-based con-nection-less link is useful for video streaming.

The video packets are streamed at the estimate of net-work bandwidth by the Dynamic Resource Allocation (DRA)block as explained later. It makes dynamic decisions on trans-mitting important video blocks at different bit rates. Theinput video from the video camera is segmented into var-ious VBs by using a localized TAMOG approach (L-TA-MOG). This approach is useful in segmenting out thread likemotion in cases like position control or speed control exper-iments. The various VBs are then encoded using scalablewavelet-based encoding which enables a layered architecturefor video transmission over the Internet. User preferences arethe input parameters to CEZW coder. The Experiment Con-troller is useful to control and coordinate the various user

Fig. 3 Block diagram oflaboratory setup

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inputs to the laboratory equipments and display the digitalinformation to the user. It also communicates the user pref-erences to the video camera and the CEZW coder.

2.3 Notify changes

Notify changes is another feature to be used when the band-width is too low for video transmission and the user has someother programs also running on the computer. He may selectthis option so that the video is updated minimally and he isnotified in case of an appreciable change in video motion.The following subsections explain the working of each indi-vidual block.

2.4 Content analysis module

Since different video content requires different QoS in videostreaming, the content analysis module also classifies framesinto two types: motion frames and non-motion frames. Videomotion frames require high image quality because the writtentext on board or slide is crucial in understanding the progressof experiment for the student. While for non-motion frames,a low quality is acceptable for the student, the motion framesare classified into various VBs. The ratio of the area of motionregion to whole image area is calculated and the framesare classified by comparing this ratio with a pre-determinedthreshold. This approach is straightforward yet sufficientlygood for such classification. The module is explained in thefollowing subsections.

2.5 VB extraction

The content analysis module analyzes video content in real-time. It classifies video content into different scenes andextracts textual content. [13] models the education classroomvideo into various objects. However, in case of laboratoryvideos, the objects are unknown beforehand. Hence we usea TAMOG-based approach. Each frame is processed usingL-TAMOG classification and the resulting regions aremasked to blocks of size 8 × 8 pixels. All useful blocks aremerged together to form a contiguous motion region. Topo-logical refinement is applied to ensure a continuous motionregion without holes. Finally, the bounding box of the con-tent region is found and matched with the previous framesin the current Group of Pictures (GOP) to ensure a constantsize and reference to the VB in the GOP.

2.6 L-TAMOG classification

We require a robust and a real time segmentation approachfor change detection in our laboratory video scenes. Sincethe background in such video is mostly static, using a sim-ple background subtraction and thresholding can detect most

of the easily visible changes in the observed scenes like inthe sample videos (a) and (b) of Fig. 1. However, in somevideo sequences like in (c) and (d) of Fig. 1, to detect subtlechanges in water level and deflection of needle, respectively,requires a more robust segmentation approach. Hence a pop-ular time adaptive mixture of Gaussians (TAMOG) methodis used which is robust and can be done online.

2.6.1 The TAMOG method

The time-adaptive mixture of Gaussians (TAMOG) method,proposed by Stauffer [14,15] is a probabilistic tool able todiscover the deviance of a signal from the expected behav-ior in an on-line fashion, with the capability of adapting toa changing background. In the general method, the temporalsignal is modeled with a time-adaptive mixture of Gaussianswith R components. The probability of observing the valuez(t) at time t is given by:

P(

z(t))

=R∑

r=1

w(t)r N

(z(t)|μ(t)

r , σ (t)r

)

where w(t)r , μ

(t)r and σ

(t)r are the mixing coefficients, the

mean, and the standard deviation, respectively, of the r thGaussian of the mixture associated with the signal at time t.At each time instant t the Gaussians are ranked in descend-ing order using the w/σ value: the most ranked componentsrepresent the “expected” signal, or the background. At eachtime instant, the ranked Gaussians are evaluated in descend-ing order (with respect to w/σ ) to find the first matchingwith the observation acquired (a match occurs if the valuefalls within 2.5σ of the mean of the component). If no matchoccurs, the least ranked component (the least important) isdiscarded and replaced with a new Gaussian with the meanequal to the current value, a high variance, and a low mixingcoefficient. If rhit is the matched Gaussian component, thevalue z(t) is labeled as foreground if

rhit∑r=1

w(t)r > T

where T is a threshold representing the minimum portion ofdata that supports the “expected behavior.” The equation thatdrives the evolution of the mixture’s weight parameters is thefollowing:

w(t)r = (1 − α)w(t−1)

r + αM (t), 1 ≤ r ≤ R

where M (t) is 1 for the matched Gaussian (indexed by rhit)and 0 for the others; the weights are renormalized at each iter-ation. Typically, the adaptive rate coefficient remains fixedover time. The μ and σ of the matched Gaussian component

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are updated with the following formulas:

μ(t)rhit

= (1 − ρ)μ(t−1)rhit

+ ρz(t)

σ 2(t)rhit

= (1 − ρ)σ 2(t−1)rhit

+ ρ(

z(t) − μ(t)rhit

) (z(t) − μ(t)

rhit

)

where ρ = αN (z(t)|μ(t)rhit , σ

(t)rhit ) The other parameters remain

unchanged. It is worth noting that the higher the adaptive rateα, the faster the model is “adapted” to signal changes.

2.6.2 The L-TAMOG modification

In remote laboratory videos, most of the changes in theobserved scene take place in some instruments, which occupyonly a portion of total observed space and their position ismore or less localized. So to optimize the run time efficiency,we restrict the search and updation in TAMOG within theregion of interest which can be determined by simple sub-traction from a background image and thresholding. By usinga low threshold value we can ensure that any probable regionis included for further refined level of analysis in TAMOGmodel. At the same time most of the pixels will be eliminatedas most of the scene, except the instruments under observa-tion, is static. Now TAMOG can be applied easily for furtherrefined classification. Since the processing is done locally ascompared to global processing done in the standard method,we call our method as local time-adaptive mixture of Gaus-sians (L-TAMOG) method.

In [14], the authors provide a statistics for real-time appli-cation of TAMOG method on SGI O2 platform where theyachieve a throughput of 15–20 frames per second on R10000processor which runs at frequency less than 250 MHz. Withthe scaling in clock frequency, emergence of rapid prototyp-ing parallel architectures such as multi-cores and FPGAs;and local processing instead of global processing of imagein our L-TAMOG scheme, we expect a tremendous speed upin implementation efficiency of L-TAMOG on modern com-puting platforms. In [15], the authors report the classificationaccuracy of TAMOG algorithm to be 66–95% depending onthe segmentation task. In our experiments, the pre-knowl-edge of expected scenario (level control video or levitationvideo) significantly increased the classification accuracy to100%.

2.7 Compression scheme

The different VBs obtained after segmentation are codedusing a modified version of the CEZW algorithm [16–18].This accounts for the rate scalability of the encoding process.The VBs are first transformed to the Y, U, V color spaceso that CEZW can exploit the interdependence between thecolor components [19]. The U and V components contain ahigh degree of redundancy and are therefore CEZW codedafter 4:2:0 down sampling. Since the maximum information

content is present in the Y component, it is given more impor-tance by coding at a bit rate that is s times the bit rate allocatedto the U and V components where s is the scale factor.

The compressed bit stream consists of the initial thresh-old T, followed by the resulting symbols from the dominantand subordinate passes of the CEZW algorithm, which areentropy coded using an arithmetic coder. The frames usedfor prediction of subsequent frames are decoded using thebase layer data stream for the VO rather than the entire VOto ensure valid motion compensation in case of changingnetwork conditions.

2.8 Frame reconstruction

Frame reconstruction is done at the user end. Different VBshave been scalably encoded and transmitted at differentbitrates. At the decoder end each of the VB is first decodedand then it is superimposed to the reconstructed backgroundframe. This is referred as “Video Integration” in Fig. 2. Since,we have used rectangular VBs instead of arbitrary shapedVBs in MPEG-4 and other schemes, at the edges we findthere is some difference in the pixel values with the back-ground values. This may lead to appearance of the VB as asuperimposed block over the background. To fix this prob-lem we do a simple averaging of the VB pixels and the back-ground frames and this is referred to as the “smootheningoperation”.

3 Dynamic resource allocation

The desired objective of video coding for real time labora-tory videos is to achieve the continuous curve that parallelsthe distortion–rate curve with a single bit stream [20]. In thissection, we briefly introduce a network aware content-basedscheme that can achieve this goal. We utilize the virtues ofDRA that adaptively manages the bandwidth allocation tothe different VBs according to their relative importance andtheir perceptible quality (Fig. 4).

We explained in the earlier sections how the input uncom-pressed video was first segmented into different VBs by thesegmentation module and then compressed using a rate scal-able codec. CEZW coder was used for illustration purposes.The encoding stage of scalable coder involves many stepsincluding frame prediction, formation of I, P and B framesduring frame packaging, motion estimation and compensa-tion, etc. Since these are not the main contributions of ourwork we have skipped the details of implementation whichcan be referred in [13,17–19].

Now, we discuss the DRA Module which interfaces thenetwork transmission and video encoder. Some of the desir-able properties of DRA are as follows:

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Fig. 4 Dynamic resourceallocation scheme

1. It must be able to keep track of changes in network band-width and always transmit data to the network at presentbandwidth of the network. For example, it must be ableto track and predict the network bandwidth which wouldenable us to avoid both network congestion and underutilization problems.

2. DRA must incorporate the knowledge of different VBswhich allows us to encode them independent of eachother and dependent on importance and requirement ofindividual VB.

3. DRA must allocate Network bandwidth to different userssuch that the sum bandwidth is equal to bandwidth esti-mate.

4. DRA must provide real-time support for video stream-ing. Therefore implementation must be simple yet robust.

With these objectives, we design a simple DRA scheme.The network traffic is decided based on:

(a) Available Network Bandwidth.(b) Relative Motion in the VB.(c) User Preference.

Much research has been done in literature on networkbandwidth estimation and a number of tools have been pro-posed which can be integrated with our approach. Theseset of tools can be broadly categorized into two sets: Theprobe gap model (PGM) exploits the information in the timegap between the arrivals of two successive probes at thereceiver and the probe rate model (PRM) is based on theconcept of self-induced congestion [21–23]. DRA closelytracks the unpredictable bandwidth variations due to heter-ogeneous access-technologies of the receivers (e.g., analogmodem, cable mode, xDSL, etc.) or due to dynamic changesin network conditions (e.g., congestion events).

3.1 Working

The CEZW encoding used in compression block creates abase and enhancement layer bit-stream. The base layer bit-stream provides compression and the enhancement layer bit-stream provides scalability feature.

The VBs were identified by L-TAMOG classificationblock and encoded using CEZW algorithm. The encodingdivided video into set of frames called as GOP or Group ofPictures. VBk

i refers to kth VB in i th GOP. Let K be thetotal number of VBs in the current GOP. k = 0 refers to thebackground frame.

We have considered a static size of VB for one GOPand the information is sent at the beginning of each GOP.�k

i denotes the relative area of kth VB in i th frame. �ki =

ar(VBki )/ar(Fi )

K∑k=1

�ki = 1∀i ∈ [1, GOP]

The above equation ensure that the areas are normalized.Thus �k

i is defined by the following equation:

�ki = ar(VBk

i )/

ar(Fi )

where ar(VBki ) denotes the area of selected VB. The motion

in each block is measured by the metrics φki .

φki = max

1≤k≤GOPabs

(�k+1

i − �ki

),

i.e., φki is defined as the maximum change in size of subse-

quent frames for kth VB in i th GOP. The degree of motionof kth VB is used to allocating a higher bandwidth to it.

We define the normality factor αi as follows:

αi =(B∗

est − Bbl)/∑

allk

{(�k

i × k × φki + Ek

i + Pk)}

Here, B∗est denotes the estimate of network bandwidth at

time of encoding i th frame and Bbl denotes the base layerbandwidth. Pk is the user specified preference for kth VBdefault taken as 0. Ei

k and �ki denotes the energy of the error

frame (or the mean square value of error frame) for VB andits growth in shape, hence the degree of motion in the motionblock.

Finally, the bandwidth is allocated to i th VB using thefollowing rule:

Bik = ς × f (i, k) × (Qk + Ei

k)

BWki = αi ×

{(�k

i ×(

k × φki

)+ Pk

)}

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Fig. 5 Average bitrate requiredfor a Helicopter, b MagneticLevitation and c Level controlvideos (without DDA) formotion and non-motion frames

where k varies over all available VBs for GOP and Bi0 =

1 − ∑Kk=1 Bi

k .

3.2 Details

The motion in a VB is tracked in our scheme by two parame-ters : φk

i and Eki . The former tracks the change in size of VB

owing to motion toward/away from the camera. The lattertracks the change in pixel intensity and essentially measuresthe change in attributes of the VB.

The background domain information available for sce-narios like remote labs (where we know the attributes ofimportant VBs is used to pre-assign the priorities in VB byassigning higher k to important and desirable VBs. The inputPk is a user input and initialized to zero. It allows the userto give a feedback and allow him to increase the allotted“perceptual” importance of the VB.

4 Experiments

Motion estimation and compensation blocks, CEZW coder,arithmetic coding and DDM module were simulated onMATLAB7. We use the peak signal-to-noise ratio (PSNR),based on the mean-squared error (MSE), as our “quality”measure. The PSNR of a YUV image is obtained by using

the following equation:

PSNR

= 10 × log10

(2552 × 1.5

/MSEY + MSEu/4 + MSEV /4

)

Here, MSEY , MSEU and MSEV are the mean square errorsof the Y, U, and V component of the reconstructed framewith respect to the original frame. The PSNR values can bemapped to ITU-R Quality and Impairment Scale and MOS by[24,25]. MOS is the human impression of the video quality,which is given on a scale from 5 to 1 [25].

Results over three video sequences have been shown.Video 1 has motion of a helicopter at burst intervals whichempowers frame skipping also. However, for this video themotion frames require high bit rate (in Kbps or in bits perpixel (bpp)). Video 2 has motion of ball under levitation andit has little non-motion frames. Video 3 has very few non-motion frames but it requires low bandwidth for good per-ceptual reception at user.

Figure 5 shows the results for various test videos. Theblue(plain) line shows the distinction between motion andnon-motion frames. Its higher value indicates a motion framewhile its lower value shows a frame classified as non-motionframe. The red line (marked with “+” sign) shows the requiredbandwidth for a PSNR value of 15 dBs. Figures 6 and 7 showthe results with Magnetic Levitation and Level control vid-eos. The required bit rate is less for Level control video as

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Fig. 6 a Original frame; b, c, d Reconstructed frames for bit rate of 0.1, 0.2 and 0.5 bits per pixel (bpp)

Fig. 7 a Original frame; b, c, d Reconstructed frames for bitrate of 0.1, 0.2 and 0.4 bpp

Fig. 8 a Original frame; b, c, d Reconstructed frames for bitrate of 0.1, 0.2 and 0.3 bpp

the VB is small compared to other videos. They howeverhave a little non-motion frames and a constant bandwidth isrequired.

Figures 6, 7 and 8 show the reconstructed frames for vari-ous bpp for the three test videos. It is evident that L-TAMOGclassification helps in selective transmission of motion regionhence, high quality video is recovered even at low bpp.

The performance comparison of our classification algo-rithm over MPEG-4 and other standards illustrates its highefficiency specifically for videos with small VBs. The LevelControl Video when encoded for a constant PSNR of 30 dBsrequired 0.72 bpp for Microsoft MPEG4 v2, 0.76 bpp forWindows media and 1.02 bpp for MPEG2 encoder. Ourscheme obtained this performance for 0.51 bpp.

Figure 9 shows the performance of DDM for level controlvideo sequence. The perceptual quality of received video ismaintained to the possible maximum level while bandwidthis varying. The experiment was performed in MATLAB usingsimulated values of Network Bandwidth as input. The blueline shows the present Network Bandwidth.

For demonstrational purposes we have simulated band-width with sharp changes in a few number of changes. Theproposed DRA module is able to achieve a good recon-struction image quality with the change in network parame-ters. Thus, the viewer was provided perceptually best qualityvideo within network constraints while carefully avoidingnetwork congestion. Comparing this with any other videostreaming protocol, since these schemes make an initial

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Fig. 9 Results for level control video with dynamic resource allocationand bandwidth estimation

assumption of Network bandwidth and transmit videoaccording to that estimate, there would be little variation inreconstruction video quality.

The real media algorithm considers two profiles: low band-width profiles which it switches between to and fro dependingon network bandwidth. Therefore, their codec also will notbe able to ensure maximum utilization of available networkbandwidth.

In the beginning of this paper, we aimed to developeda scheme that is able to give better quality at same band-width. The virtues of VB-based compression have been usedto make this possible. We used L-TAMOG algorithm to seg-ment out the VBs and coded them individually. Figures 6,7, and 8 illustrate how our algorithm is able to give goodperformance at low bpps and that the reconstruction qual-ity at motion regions of the image is ensured to be consid-erably high to avoid “blockiness” or appearance of blocksin the moving regions. We illustrated how our algorithmrequires lower bitrate than windows media and MPEG-4schemes.

5 Conclusion

The proposed compression and transmission scheme prom-ises efficient realization of remote laboratories. This papercontributes a new a localized search concept to find motionblocks in low motion videos and exploits it to optimally uti-lize the available bandwidth to provide the client with mostrelevant visual information.

The system proposed can be made more robust by consid-ering better Network Bandwidth estimation tools and morerobust scalable coders. Other motion block recognition tech-niques can be used in addition to the proposed one to moreaccurately determine the motion or VBs.

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