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Time-Compression: System Concerns, Usage, and Benefits. Nosa Omoigui, Li-wei He, Anoop Gupta, Jonathan Grudin, and Elizabeth Sanocki Microsoft Research. Introduction. Digital multimedia content is becoming widespread Unlike text, multimedia is not easy to skim
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Time-Compression:System Concerns, Usage, and Benefits Nosa Omoigui, Li-wei He, Anoop Gupta, Jonathan Grudin, and Elizabeth Sanocki Microsoft Research
Introduction • Digital multimedia content is becoming widespread • Unlike text, multimedia is not easy to skim • Ability to skim is a key advantage of digital multimedia • Time-compression • Skimming • We focus on time-compression
Time-Compression Basics • Reduces the time it takes to listen to or watch multimedia • Audio time-compression • Signal processing issues are well understood • Re-sampling results in an increase in pitch • Pitch-preserving techniques: • Segment abutting, OLA, SOLA, P-SOLA • Video time-compression • Relatively straightforward • Drop frames or change frame rendering rate
Time-Compression Issues • Signal processing technology already exists • However, not found in client-server environments • Several possible design techniques • Multiple server-side files • Simple client-side solution • Sophisticated client-side solution • Systems concerns include speedup-factor granularity, latency, storage, and bandwidth
Multiple Server-Side Files • Advantages • No additional bandwidth demands • Relatively simple to implement • Disadvantages • Discrete speed-up factor granularity • Latency while switching speedup-factors • Requires additional storage • Does not work with existing media files
Simple Client-Side Solution • Advantages • Continuous granularity • No additional storage demands • Works with existing media files • Disadvantages • Requires additional bandwidth • Some client-side complexity • Latency while switching speedup-factors
Sophisticated Client-Side Solution • Advantages • Continuous granularity • No additional storage demands • Works with existing media files • No latency while switching speedup-factors • Disadvantages • Requires additional bandwidth • Significant client-side complexity
Focus of Our Study • Impact of engineering trade-offs on user behavior • Speedup factor adjustment granularity • Latency for speedup adjustment • User behavior: time saved, number of adjustments, perception on the feature, etc.
Experimental Procedure • Dimensions for comparison • Continuous vs. discrete speedup factor adjustment • Long latency vs. short latency • User study of 15 subjects • Task: watch video and give a verbal summary • Five 25-40 minute videos • 2 Discovery channel videos and 3 presentations from ACM 97 • Five conditions (within subject; counter-balanced)
Speedup Factor Achieved • Results • No significant differences across conditions • Average speedup factor is 1.42 • Savings of about 45 minutes for 2 hours of video • Substantial variation across subjects (1.18 – 1.65) • Key implication • Implementers should feel free to choose the simplest solution
Savings in Task Time Are Significant • Our question • Would time for reviewing outweigh time saved using compression? • Method • Instrument the UI to log subjects’ action • Divide the task time into 5 categories: • View, review, pause, seek, and latency • Results • Review time goes up with time-compression • 9-11% vs. 6% • Still substantial savings in task time achieved • 22% savings over no-time-compression
Number of Speedup Adjustments • Our hypotheses • More adjustments at the beginning of the talk • More adjustments for the lower-latency conditions • Observations • Adjustments do tend to occur more in the beginning • No significant difference across conditions
User Feedback and Comments • 87% either loved the feature or found it very useful • Experienced subjects found the normal speed too slow • “Once people have experienced time compression, they will never want to go back” • Employ time-compression with other features • E.g. bookmarks, table of content, and instant-replay button
Conclusions • Subjects loved the time-compression feature • Significant savings in task time is achievable • Our study shows 22% savings • Implementers should feel free to choose the simplest solution of the three trade-offs • No significant differences across conditions