Virtual Contact Sheets Wolfgang Richter Kiryong Ha, Alok Shankar, Ardalan Amiri Sani*, Jan Harkes, Lin Zhong*, Mahadev Satyanarayanan PARALLEL DATA LABORATORY Carnegie Mellon University *Rice University Crowdsourcing the News [1] “When every cell phone can record video and take pictures, everyone is a potential news source.” Who killed Ian Tomlinson? http://www.pdl.cmu.edu/ 2 Wolfgang Richter © July 16 CNN: Caught Red Handed [2] I’m a sneaky thief. http://www.pdl.cmu.edu/ 3 Wolfgang Richter © July 16 Problem Older, uploaded Cloud Recent, not uploaded http://www.pdl.cmu.edu/ 4 Wolfgang Richter © July 16 Fundamental Mobile Challenges • Real-time sync not possible: How far can we degrade the • Energy objects in life a desired search before it • Infinite battery loses meaning? • Bandwidth • Increasingly metered and charged http://www.pdl.cmu.edu/ 5 Wolfgang Richter © July 16 Just-in-Time Search 1. Check virtual contact sheet • Collection of fidelity-reduced images in the cloud 2. Pull full fidelity image on hit • Search full fidelity; present full fidelity results to searcher http://www.pdl.cmu.edu/ 6 Wolfgang Richter © July 16 Near Real-Time Sync (1) Search hit (2) Retrieve http://www.pdl.cmu.edu/ Cloud 7 Wolfgang Richter © July 16 Contact Sheet Analogy http://www.pdl.cmu.edu/ 8 Wolfgang Richter © July 16 Evaluating Feasibility [3] Benchmark Face Detection, RGB Histogram, Brightness Filter Varying Resolution and JPEG Quality Factor Using http://www.pdl.cmu.edu/ 9 Wolfgang Richter © July 16 Energy Savings 10 images = 5 seconds call time (Droid) http://www.pdl.cmu.edu/ 10 Wolfgang Richter © July 16 Bandwidth Savings Orders of magnitude savings in bandwidth. http://www.pdl.cmu.edu/ 11 Wolfgang Richter © July 16 What about Search Accuracy? Face Detection No one-size-fits-all sweet spot. http://www.pdl.cmu.edu/ 12 Wolfgang Richter © July 16 Conclusion & Future Work • Searches retain meaning • No one-size-fits-all, but • Orders of magnitude bandwidth savings • Significant energy savings • More search primitives? • Perceptual hashing • Face recognition • Texture Filters • Better fidelity reduction? • Keep faces at high fidelity http://www.pdl.cmu.edu/ 13 Wolfgang Richter © July 16 Further Reading [1] TED. http://www.ted.com/talks/paul_lewis_crowdsourcing_the_news.html. [2] CNN. http://www.cnn.com/2010/CRIME/08/24/new.jersey.theft.photo/index.html?hpt=C1. [3] SimpleCV. http://simplecv.org/. Jan Harkes, Mahadev Satyanarayanan, Ardalan Amiri Sani, and Benjamin Gilbert. A Contact Sheet Approach to Searching Untagged Images on Smartphones. CMU Technical Report CMU-CS-11-132. Ardalan Amiri Sani, Wolfgang Richter, Xuan Bao, Trevor Narayan, Mahadev Satyanarayanan, Lin Zhong, and Romit Roy Choudhury. Opportunistic Content Search of Smartphones. Rice University Technical Report TR0627-2011. Xuan Bao, Trevor Narayan, Ardalan Amiri Sani, Wolfgang Richter, Romit Roy Choudhury, Lin Zhong, and Mahadev Satyanarayanan. The Case for Context-Aware Compression. HotMobile 2011. Jason Flinn and Mahadev Satyanarayanan. Energy-aware Adaptation for Mobile Applications. SOSP 1999. Y. Nimmagadda, K. Kumar, and Y. H. Lu. Energy-efficient image compression in mobile devices for wireless transmission. ICME 2009. http://www.pdl.cmu.edu/ 14 Wolfgang Richter © July 16 Full Fidelity Average photo size: 374 KB 12,000+ Flickr dataset http://www.pdl.cmu.edu/ 15 Wolfgang Richter © July 16 Reduce Resolution Average photo size: 221 KB 12,000+ Flickr dataset http://www.pdl.cmu.edu/ 16 Wolfgang Richter © July 16 JPEG Quality Factor Average photo size: 6 KB 12,000+ Flickr dataset http://www.pdl.cmu.edu/ 17 Wolfgang Richter © July 16 Bandwidth Savings Table Resolution 100 95 75 50 25 5 640x480 221 KB 114 KB 47 KB 31 KB 19 KB 6 KB 320x240 61 KB 33 KB 14 KB 10 KB 6 KB 2 KB 160x120 17 KB 10 KB 5 KB 3 KB 2 KB 826 B 80x60 5 KB 3 KB 2 KB 1 KB 892 B 460 B 40x30 2 KB 1 KB 725 B 588 B 484 B 345 B 20x20 1000 B 727 B 501 B 436 B 385 B 316 B http://www.pdl.cmu.edu/ 18 Wolfgang Richter © July 16