Swift: Reducing the Effects of Latency in Online Video Scrubbing

Justin Matejka, Tovi Grossman, George Fitzmaurice

January 2012 · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI)

Abstract

We first conduct a study using abstracted video content to measure the effects of latency on video scrubbing performance and find that even very small amounts of latency can significantly degrade navigation performance. Based on these results, we present Swift, a technique that supports real-time scrubbing of online videos by overlaying a small, low resolution copy of the video during video scrubbing, and snapping back to the high resolution video when the scrubbing is completed or paused. A second study compares the Swift technique to traditional online video players on a collection of realistic live motion videos and content-specific search tasks which finds the Swift technique reducing completion times by as much as 72% even with a relatively low latency of 500ms. Lastly, we demonstrate that the Swift technique can be easily implemented using modern HTML5 web standards.

DOI PDF

Figures

Figure 1. An illustration of the scrubbing behavior of a traditional streaming video player and the Swift player. With the Swift system a quick-to-download low resolution version of the video is displ

PC version of the Netflix streaming vide

Figure 3. Vidbeginning of most recentlyscene, was oumarked with

Figure 4. First three scenes in a sequential scene order- ing. The order which the squares fill in is fixed.

Figure 5. First three scenes in an ordered scene ordering. The order which the squares fill in is random.

Figure 6. First three scenes in a random scene ordering. The order which the squares fill in is random.

Figure 7. Average median navigation completion times for each combination of video type and ns-latency. (Note: error bars report standard error).

Figure 8. Average median navigation completion time divided into groups based on video type. (Note: error bars report standard error).

Figure 9. Average number of frames seen per trial.

Figure 10. Encoded file sizes using varying resolutions and frame counts.

Figure 11. Frames taken from the target scenes for each video type and discernibility combination used in the study. For the ordered examples, (A) is from before the change occurring, and (B) is from after. In the random examples, (C) is a typical scene from the movie and (D) is the target scene.

Figure 12. Results for the three video types. (Note: error bars report standard error).

BibTeX

@inproceedings{10.1145/2207676.2207766,
 abstract = {We first conduct a study using abstracted video content to measure the effects of latency on video scrubbing performance and find that even very small amounts of latency can significantly degrade navigation performance. Based on these results, we present Swift, a technique that supports real-time scrubbing of online videos by overlaying a small, low resolution copy of the video during video scrubbing, and snapping back to the high resolution video when the scrubbing is completed or paused. A second study compares the Swift technique to traditional online video players on a collection of realistic live motion videos and content-specific search tasks which finds the Swift technique reducing completion times by as much as 72% even with a relatively low latency of 500ms. Lastly, we demonstrate that the Swift technique can be easily implemented using modern HTML5 web standards.},
 address = {New York, NY, USA},
 author = {Matejka, Justin and Grossman, Tovi and Fitzmaurice, George},
 booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
 doi = {10.1145/2207676.2207766},
 isbn = {9781450310154},
 keywords = {online streaming, video, video navigation},
 location = {Austin, Texas, USA},
 numpages = {10},
 pages = {637–646},
 publisher = {Association for Computing Machinery},
 series = {CHI '12},
 title = {Swift: Reducing the Effects of Latency in Online Video Scrubbing},
 url = {https://doi.org/10.1145/2207676.2207766},
 year = {2012}
}