Transform recipes for efficient cloud photo enhancement
Name
xform_paper_sigasia2015.pdf
Size
62.68 MB
Format
Adobe PDF
Checksum (MD5)
5556a068920f6b1566e73f0feb4c19a6
Author(s)
Shih, YiChang
Chaurasia, Gaurav
Ragan-Kelley, Jonathan
Paris, Sylvain
Gharbi, Michael Yanis
Durand, Fredo
Date Issued
November 2015
Journal
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Citation
Michael Gharbi, YiChang Shih, Gaurav Chaurasia, Jonathan Ragan-Kelley, Sylvain Paris, and Fredo Durand. 2015. Transform recipes for efficient cloud photo enhancement. ACM Trans. Graph. 34, 6, Article 228 (October 2015), 12 pages.
Version
Author's final manuscript
Abstract
Cloud image processing is often proposed as a solution to the limited computing power and battery life of mobile devices: it allows complex algorithms to run on powerful servers with virtually unlimited energy supply. Unfortunately, this overlooks the time and energy cost of uploading the input and downloading the output images. When transfer overhead is accounted for, processing images on a remote server becomes less attractive and many applications do not benefit from cloud offloading. We aim to change this in the case of image enhancements that preserve the overall content of an image. Our key insight is that, in this case, the server can compute and transmit a description of the transformation from input to output, which we call a transform recipe. At equivalent quality, our recipes are much more compact than JPEG images: this reduces the client's download. Furthermore, recipes can be computed from highly compressed inputs which significantly reduces the data uploaded to the server. The client reconstructs a high-fidelity approximation of the output by applying the recipe to its local high-quality input. We demonstrate our results on 168 images and 10 image processing applications, showing that our recipes form a compact representation for a diverse set of image filters. With an equivalent transmission budget, they provide higher-quality results than JPEG-compressed input/output images, with a gain of the order of 10 dB in many cases. We demonstrate the utility of recipes on a mobile phone by profiling the energy consumption and latency for both local and cloud computation: a transform recipe-based pipeline runs 2--4x faster and uses 2--7x less energy than local or naive cloud computation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2816795.2818127
