A Novel Rate-Scalable Multimedia Service for E-Learning Videos using Content Based Wavelet Compression

Sood, Aparesh; Sarthi, Divya; Pande, Amit; Mittal, Ankush

doi:10.1109/indcon.2006.302843

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…the algorithm minimized the temporal and spatial redundancies in lecture videos and gives an optimum level of compression. Sood and Aparesh [7] introduced a noble concept of even and odd frame distinction (EODist). A significant reduction in number of retransmissions of data packets observed after integrating EODist with conventional mechanism, which is also a potential advantage of their algorithm in bandwidth deficient wireless network environment.…”

Section: Image/video Compression Technique For E-learningmentioning

confidence: 99%

Effectiveness of the Image/Video Compression and Its Role in Supporting e-Learning Systems

Abd-Elhafiez

Heshmat

2013

2013 Fourth International Conference on E-Learning "Best Practices in Management, Design and Development of E-Courses: Standard

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In this paper, we address the problem of delivery of image and multimedia services on bandwidth deficient wire and wireless networks for E-learning. Became exponential growth in the volume of image and multimedia limits the ability of companies to manage this data, image and multimedia and control effectively, and with continued high storage costs make the companies having problems retrieving image and multimedia. This research aims to address these items discussion and analysis: concepts of image/video compression, the characteristics of image/video compression, the benefits and constraints of the use of image/video compression technique for E-learning lecture videos. Also, a efficient image and video compression technique is presented as a tool, which may be used in E-learning systems.

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Section: Image/video Compression Technique For E-learningmentioning

confidence: 99%

Effectiveness of the Image/Video Compression and Its Role in Supporting e-Learning Systems

Abd-Elhafiez

Heshmat

2013

2013 Fourth International Conference on E-Learning "Best Practices in Management, Design and Development of E-Courses: Standard

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“…It classifies video content into different scenes and extracts textual content. [13] models the education classroom video into various objects. However, in case of laboratory videos, the objects are unknown beforehand.…”

Section: Vb Extractionmentioning

confidence: 99%

“…The encoding stage of scalable coder involves many steps including frame prediction, formation of I, P and B frames during frame packaging, motion estimation and compensation, etc. Since these are not the main contributions of our work we have skipped the details of implementation which can be referred in [13,[17][18][19]. Now, we discuss the DRA Module which interfaces the network transmission and video encoder.…”

Section: Dynamic Resource Allocationmentioning

confidence: 99%

Content-based network resource allocation for real time remote laboratory applications

Mittal

Pande

Kumar

2009

SIViP

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This paper presents a practical solution to make remote laboratories a realizable dream. A remote laboratory is an online laboratory where students can get first-hand experience of engineering labs via Internet. Video transmission can provide hands on experience to the user but the transmission channel or networks typically have variable and low bandwidth that poses a tough constraint for such implementation. This work presents a practical solution to such problems by adaptively transmitting the best available quality of laboratory videos to the user depending on network bandwidth. The concept behind our work is that not all objects or frames of the video have equal importance, and thus bandwidth reduction can be accomplished by intelligently transmitting important parts at relatively higher resolution. A localized Time adaptive mean of Gaussian (L-TAMOG) approach is used to search for moving objects which are then allocated network resources dynamically according to the varying network bandwidth variations. Adaptive motion compensated wavelet-based encoding is used to achieve scalability and high compression. The proposed system tracks the network bandwidth and delivers optimally the most important contents of video to the student. Experimental results over several remote laboratory sequences show the efficiency of the proposed framework.

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“…Hardware architectures have been proposed for mobile video compression and encryption [9][10][11]. Efficient video transmission in cellular low bandwidth scenarios has also been discussed [12][13][14][15][16]. With 4G LTE becoming popular, we attempt to understand the features of LTE which help in high quality video transmission.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Video Communications Over 4G Network

Pande¹

2014

Intelligent Computing, Networking, and Informatics

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With exponential increase in the volumes of video traffic in cellular networks, there is an increasing need for optimizing the quality of video delivery. Fourth-generation networks (Long-Term Evolution-Advanced or LTE-A) are being introduced in many countries worldwide, which allow a downlink speed of up to 1 Gbps and uplink of 100 Mbps over a single base station. In this paper, we characterize the performance of LTE-A physical layer in terms of transmitted video quality when the channel conditions and LTE settings are varied. We test the performance achieved as the channel quality is changed and HARQ features are enabled in physical layer. Blocking and blurring metrics were used to model image quality.

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A Novel Rate-Scalable Multimedia Service for E-Learning Videos using Content Based Wavelet Compression

Cited by 9 publications

References 20 publications

Effectiveness of the Image/Video Compression and Its Role in Supporting e-Learning Systems

Effectiveness of the Image/Video Compression and Its Role in Supporting e-Learning Systems

Content-based network resource allocation for real time remote laboratory applications

Performance Evaluation of Video Communications Over 4G Network

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