2233091
Project Grant
Overview
Grant Description
Sbir Phase I: Improved Image Compression Targeting Machine Learning Based Detection Algorithms -The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in reducing the sizes of Earth observation images, thereby saving millions of dollars on transmission, storage, and processing costs.
Storage and processing costs account for a large portion of costs for Earth observation companies. Reducing these costs will make Earth observation imagery more accessible for a broader audience, including companies performing climate and environment studies.
The need for reduced image sizes also extends to video compression. Video calls, telepresence, telemedicine, remote work, and metaverse all depend on the ability to stream video over limited or variable bandwidth connections. This technology will find ready applications in the area of high-efficiency video compression.
This SBIR Phase I project uses object detection algorithms to detect areas of importance in an image and utilizes that information to improve the efficiency of image compression. More specifically, the research will develop a compression network that maximizes the detection accuracy of a downstream, machine learning-based object detector.
In contrast, current compression algorithms do not interpret the images they are compressing and simply minimize a visual loss function that treats the entire image equally. The technology will produce images that can be stored in the standard image compression file formats, including .PNG and .JPEG.
This technology will enable fast compression and will explore modified compression architectures, quantization, pruning, and parallelization using graphics processing units to reduce latency of compression.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Storage and processing costs account for a large portion of costs for Earth observation companies. Reducing these costs will make Earth observation imagery more accessible for a broader audience, including companies performing climate and environment studies.
The need for reduced image sizes also extends to video compression. Video calls, telepresence, telemedicine, remote work, and metaverse all depend on the ability to stream video over limited or variable bandwidth connections. This technology will find ready applications in the area of high-efficiency video compression.
This SBIR Phase I project uses object detection algorithms to detect areas of importance in an image and utilizes that information to improve the efficiency of image compression. More specifically, the research will develop a compression network that maximizes the detection accuracy of a downstream, machine learning-based object detector.
In contrast, current compression algorithms do not interpret the images they are compressing and simply minimize a visual loss function that treats the entire image equally. The technology will produce images that can be stored in the standard image compression file formats, including .PNG and .JPEG.
This technology will enable fast compression and will explore modified compression architectures, quantization, pruning, and parallelization using graphics processing units to reduce latency of compression.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Saratoga,
California
95070-5718
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
AI Acuity was awarded
Project Grant 2233091
worth $274,623
from National Science Foundation in August 2023 with work to be completed primarily in Saratoga California United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Improved image compression targeting machine learning based detection algorithms
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in reducing the sizes of earth observation images, thereby saving millions of dollars on transmission, storage and processing costs. Storage and processing costs account for a large portion of costs for earth observation companies, reducing these costs will make earth observation imagery more accessible for a broader audience including companies performing climate and environment studies. The need for reduced image sizes also extends to video compression. Video calls, telepresence, telemedicine, remote work and metaverse all depend on the ability to stream video over limited or variable bandwidth connections. This technology will find ready applications in the area of high-efficiency video compression._x000D_ _x000D_ This SBIR Phase I project uses object detection algorithms to detect areas of importance in an image and utilizes that information to improve the efficiency of image compression. More specifically, the research will develop a compression network that maximizes the detection accuracy of a down-stream, machine learning-based object detector. In contrast, current compression algorithms do not interpret the images they are compressing and simply minimize a visual loss function that treats the entire image equally. The technology will produce images that can be stored in the standard image compression file formats including .png and .jpeg. This technology will enable fast compression and will explore modified compression architectures, quantization, pruning and parallelization using graphics processing units to reduce latency of compression._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
SP
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 8/17/23
Period of Performance
8/15/23
Start Date
7/31/24
End Date
Funding Split
$274.6K
Federal Obligation
$0.0
Non-Federal Obligation
$274.6K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2233091
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
X5RMKLZLLAL9
Awardee CAGE
None
Performance District
CA-16
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,623 | 100% |
Modified: 8/17/23