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Adaptation of

Lecture Videos to Mobile Device

Presented by:

Anisha Nayak & Akanksha Patil



Introduction

•Mobiles and PDAs are becoming ubiquitous,

specially amongst students and there’s a need for

having all the applications of our real world into our

pocket!

•One such application is viewing lecture videos on

your mobile or PDAs.

•These videos are typically meant for the

computers and not hand held devices like mobile

phones, hence our proposal is to disprove this and

bring the above said need to reality!!



Problem Statement

Lecture videos are traditionally designed for

desktop computers, these require high video bit-

rates, due to which viewing them on low network

bandwidth connections like GPRS incurs long delays

and high costs. So the challenge is to come up with

some innovation in order to map these high BW

videos onto the available BW.



CHALLENGES IN IMPLEMENTATION

There are two main challenges or problems that areencountered in this adaptation process:

1. Network Bandwidth and Cost - Network connections (likeGPRS) that are used by mobile phones have a very lownetwork bandwidth (40kbps). This is very low as compared tothe bit rate of a video which is in the range of 400-1200kbps

or even higher. Higher the bandwidth, higher you need to paythe service provider.

2. Usability of the content - Lecture videos usually have

written material, either as presentation slides or in thewritings of the instructor. Hence, the adaptation process mustensure that understanding and visibility of the content mustnot be compromised.



Technical terms :•VIDEO TRANSCODING:

Video transcoding is the process of converting the video

from one compression format to another. In the process,video parameters like bit rate, frame rate and resolution can

be changed to meet the target device requirements.

2. CONTENT AWARE VIDEO ADAPTATION:

Content aware video adaptation is the process of adapting

the video based on its content, so that the content is visible

clearly, even at lower bit rates.But, compressing to very low bit rates degrades the

quality for any compression format, and can make the

content incomprehensible. Hence the above said process is a

trade off between compression and clarity of data.



Current work in the field :1. There have been attempts, to identify important segments of a video

and important objects or regions in a video frame sequence.

These are then encoded at a high quality and other

portions are encoded at a low quality.These methods achieve a descent video quality (i.e.

that looks as good as the original one) at moderate bit rates of 100kbps for non-lecture videos, but at very low bit rates of 40kbpsand in the context of lecture videos, the quality and visibility of

written content is doubtful, as the video quality might degradefurther.

2. In the second method, non-changing portions of a lecture videoare identified and one image is extracted from each such region.

The output is a slide-show of such images alongwith the audio stream of the whole lecture. This method basicallyaims to achieve reduction in bandwidth by eliminatingredundancies in the video.



Solution for the problem

We employ the same concept of

displaying slide-show of images extracted

from the video, as done in previous workbut in a different way. We identify Study

Elements within the video.



BUT WHAT ARE STUDY ELEMENTS?

STUDY ELEMENTS

• “Study Element” is the portion of video showing any medium of instruction.

• Medium of instruction could be a ‘slide of presentation’ or ‘anexplanation written by a instructor on a white paper’ or the instructorherself explaining something.

• We then extract images from each Study-Element at a differentintervals; for example one image every 5seconds.

• The output is the slide-show of the extracted images sent to the client

according to the interval at which they were extracted from theelement, along with the audio.

• The user can specify the user experience desired for each Study-Element and has the flexibility for choosing the network bandwidthavailable as input.



System architecture• The adaptation methodology accepts the

video as input.

•It further accepts the desired user experienceand the network bandwidth available, asparameters from the user.

• The desired user experience value for everyStudy-Element is accepted.

• For example, the desired user experience

values for presentation element andinstructor element are taken as input.

• The output consists of a set of images,sending interval for each Study-Element andcontinuous audio.

• Images are sent to the client device according

to the sending• interval. For example, if the sending interval is

five seconds, then one image is sent every fiveseconds.

• The audio is continuously streamed to theclient.



We have defined three types of Study-Elements

1) Presentation Element - Portion of video

that shows one slide of a presentation2) White Paper Element - Portion of video

that shows white paper on which

instructor is writing something

3) Instructor Element - Portion of videothat shows instructor talking Sample

images of the elements are shown in

Figure below.

Study elements are portions of videothat are different from one another in

terms of the following properties:



User Experience

• We define “User Experience” as a value between 0 and 1that

represents the desired level of user experience. This is given

as input by the user to the system.

• since images are seen with some delay, the user would be

more satisfied if that delay is lesser. Hence, a zero value

means that the delay experienced is large (to be chosen by

system administrator),and a value of one means that the

delay experienced is equal to one second.



Calculating system parameters

• Calculating Sending Intervals

• User Experience of Study-Elements

•

Calculating network bandwidth and size• Finding Output Sending Interval



User Experience of Study-Elements

1) Presentation Element: 2) White Paper Element:

Delay Experienced(D2) = Time that user

sees the image of the slide−

Time that theslide actually started in the original video

User Experience(U2) = 1 Sec

Delay Experienced

Delay Experienced(D1) = Sending interval

User Experience(U1) = 1 Sec Delay Experienced

3) Instructor Element



Calculating network bandwidth and size

Network Overhead(NO) = Image Size

Sending RateSize Overhead(SO) = Total Size Of The Images Extracted From The

element



Video Size Comparison

Comparison of Size Reduction



CONCLUSION AND FUTURE WORK

Slide show based adaptation achieves far greater bandwidth

reduction than other techniques.

It can be used to adapt lecture videos so that they can be viewed onlow bandwidth network connections.

It should be observed that we assume that video of slides is shown

because this is important for offline viewing of the lecture.

Currently, for our method, tagging the boundaries of Study-Elements is assumed done manually typically at the time of production

of video.

We feel that this process could be automated by using a technique

called shot detection, that identifies the places in the video, where

major changes occur.

We also plan to implement the idea as a working system,

and take the user experience of actual users. This could be

then compared with the values obtained from the metrics.



REFERENCES

[1] H.264. ‘‘http://ati.amd.com/products/pdf/h264 whitepaper.pdf’’.

[2] Shih-Fu Chang, Di Zhong, and Raj Kumar. Real-time content-based

adaptive streaming of sports videos. In CBAIVL ’01: Proceedings of the

IEEE Workshop on Content-based Access of Image and Video Libraries

(CBAIVL’01), page 139, Washington, DC, USA, 2001. IEEE Computer

Society.

[3] Ming-Ho Hsiao, Yi-Wen Chen, Hua-Tsung Chen, Kuan-Hung Chou,

and Suh-Yin Lee. Content-aware video adaptation under low-bitrateconstraint. EURASIP J. Adv. Signal Process, 2007(2):27 – 27, 2007.

[4] W. Tavanapong. A characteristics-based bandwidth reduction

technique

for pre-recorded videos. In IEEE International Conference on Multimediaand Exposition, page WP11, 2000.

[5] J. Xin and C-W Sun M-T. Lin. Digital video transcoding. Technical

report, Mitsubishi Electric Research Laboratories, January 2005. Digital

Video Transcoding, Proceedings of the IEEE, Vol. 93, Issue 1, pp. 84-97,

January 2005, IEEE Xplore (http://ieeexplore.ieee.org/Xplore



Thank You

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