Study-Element Based Adaptation of Lecture Videos to Mobile

Study-Element Based Adaptation of Lecture Videos to Mobile Devices Ganesh Narayana Murthy (M. Tech IIT Bombay) Sridhar Iyer (Associate Professor, IIT Bombay) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 1

Problem Definition • Adapt CDEEP videos to be viewable on mobile devices: – Viewable at low network bandwidths (like GPRS) – Viewable at low cost • Video bit-rate – – Size of video stream over time Total size = bit-rate * total time CDEEP video bit-rate: 1150 kbps GPRS bit-rate: 40 kbps • The problem: – Video playing incurs delays if available network bandwidth is less than video-bit rate Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 2

Video Transcoding • Converting from one video format to another – Changing video bit rate – Changing other parameters like frame rate, screen resolution Format Name Typical Bit Rate Application MPEG-1 1. 5 Mbps or less CD-ROM MPEG-2 5 -8 Mbps DVD, HDTV H. 263 Typically low bit rates Low bit-rate video conferencing MPEG-4 / H. 264 40 Kbps to 10 Mbps and above Internet Streaming, Video Telephony Flash Video (FLV) Typically low bit rates Embedded video in websites Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 3

Video Quality at low-bit rates (a) MPEG-1 (b) MPEG-2 Images from transcoded videos (Target bit rate : 40 kbps, No audio) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 4

Video Quality at low bit-rates (contd. ) (c) H. 264 (mp 4) (d) H. 263 (3 gpp) Images from transcoded videos (Target bit rate : 40 kbps, No audio) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 5

(e) Flash Video (flv) Images from transcoded videos (Target bit rate : 40 kbps, No audio) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 6

Comparison of Video Codecs Format Name Original Video Size Converted Video Size Video Quality at low-bit rates Remarks MPEG-1 432 MB 26 MB Poor Cannot be used at low bit-rates MPEG-2 432 MB 29. 12 MB Poor Cannot be used at low bit rates H. 263 432 MB 38. 3 MB Poor Cannot be used at low bit rates H. 264 432 MB 16. 9 MB Good Processing power / Decoding complexity is high. [1] Flash 432 MB 20. 5 MB Good Can be used, but cost is still high. (Note: Video bit rate = 1150 kbps, No audio, Target bit rate = 40 kbps, No audio) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 Video Sizes are still high for viewing over GPRS 7

Study-Element Based Adaptation Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 8

Motivation • CDEEP video usually consists of – Presentation slides – Instructor explaining on white paper – Video of instructor talking • Presentation slide is usually not changing – Video of slide is not required. One image is sufficient • Idea – Extract one image every ‘n’ seconds and send to client. – This would reduce amount of data sent for showing one slide. Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 9

Method-1 • Send one image every ‘n’ seconds – Server sends one image every ‘n’ seconds to client – Audio is simultaneously streamed • Network bandwidth and Size – Network Overhead (NO) = Image Size / n – Size Overhead (SO) = Total size of images • What is the user experience? Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 10

User Experience Basis • Presentation Study Element – Portion of video showing one slide • White Paper Study Element – Portion of video showing instructor writing on white paper • Instructor Study Element – Portion of video showing instructor talking Presentation Slide 0 3 5 10 …………. . 15 White Paper 25 30 Delay in start of slide Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 ………. . 35 Video Time (secs) 11

User Experience • Presentation Element – Delay Experienced (D 2) = • Delay in start of slide as compared to audio • White Paper Element – Delay Experienced (D 1) = • Delay between any two consecutive images = Sending Rate • Instructor Element – Only audio important. No image need be sent. • User Experience is assumed to be one • User Experience (Ui) = 1 sec / Di Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 12

Method-2 • Trade-off for user experience • Cost incurred in terms of number of images sent • Same sending interval for all elements, cannot balance user experience and cost. • Choose different sending interval for each study element • Probably: – Higher user experience for white paper element – Lower user experience for presentation element Sending Interval User Experience Cost Trade-Off Relation Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 13

System Overview Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 14

Building the index • Corpus of 10 videos – Representative of various departments • Consider different sending intervals ‘r’ – For each ‘r’ find NO, SO and U for every study element in a video. – Repeat for all videos and take average. • This relation can be used backwards: – For calculating sending interval, given network bandwidth and user experience. Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 15

Graphs of User Experience Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 16

Graphs of overheads Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 17

Results Achieved Size Reduction Original Video Size(MB) Images Size (MB) Reduction (%) U 1 U 2 Supported Network Bandwidth 432 2. 85 97% 0. 2 0. 38 20 kbps and above Fig: Video stream size reduction (note: Original video bit-rate = 1150 kbps, No audio) Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 18

Results (contd. ) Balance User Experience and Cost Sending Interval U 1 U 2 NO 1 S 01 (kbps) (MB) NO 2 SO 2 (kbps) (MB) Total Size (SO) White Paper Element Presentation Element 5 5 0. 2 0. 337 15. 12 1. 25 23. 43 1. 6 5. 46 5 15 0. 2 0. 115 15. 12 1. 25 7. 81 0. 53 1. 78 15 15 0. 067 0. 115 5. 254 0. 781 7. 63 1. 03 1. 81 Reduction in size user experience for white paper element remaining same Ganesh Narayana Murthy Required Network Bandwidth =max(NO 1, NO 2) is reduced NCC 2010, Chennai, 30/01/2010 19

Conclusion • Large size reduction can be achieved by using the concept of slideshows • Identifiying study-elements within the video helps define user-experience of the slideshow. • CDEEP Lecture videos can be adapted to low network bandwidths and in a cost-controlled manner. Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 20

Future Work Automated tagging – Identifying study element boundaries – Shot detection techniques User Experience Correlation – Identifying relation between obtained user experience and actual user values Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 21

References 1. H. 264 white paper. http: //ati. amd. com/products/pdf/h 264_whitepaper. pdf. 2. Real-time Content-Based Adaptive Streaming of Sports Videos. Shih-Fu Chang, Di Zhong, and Raj Kumar. 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. Content-aware video adaptation under low-bitrate constraint. Ming-Ho Hsiao, Yi-Wen Chen, Hua-Tsung Chen, Kuan-Hung Chou, and Suh-Yin Lee. EURASIP J. Adv. Signal Process, 2007(2): 27 -27, 2007. 4. A Characteristics-Based Bandwidth Reduction Technique for Pre-recorded Videos. Wallapak Tavanapong and Srikanth Krishnamohan. In IEEE International Conference on Multimedia and Expo (III), pages 1751 -1754, 2000. Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 22

Questions? Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 23

Content-Aware Adaptation Method Name Adaption Mechanism Video Quality Remarks Hsiao et. al. [2] Identify visual attention regions in a frame. Encode them at high quality. Poor Quality of important objects still depends on network bandwidth Chang. et. al [3] Identify events in sports videos at high quality. Other regions as slideshows. Good Slideshow of images reduces network bandwidth and size Tavanapong. et. Identify non-changing Al. [4] portions of lecture video and extract one image from them Good Exploits redundancy in lecture videos Ganesh Narayana Murthy NCC 2010, Chennai, 30/01/2010 24