Ms. Bridget Furlong Profile

Bridget Furlong

at Soar Technology Inc

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 31 May 2022 Presentation + Paper

Task-based assessment of image compression methods and products

Benjamin Purman, Matthew Nulle, Bridget Furlong

Proceedings Volume 12124, 121240C (2022) https://doi.org/10.1117/12.2617757

KEYWORDS: Data modeling, Performance modeling, Image compression, Image quality, Video, Video compression, Sensors, Mathematical modeling, Image resolution, Image classification

Read Abstract +

Remotely-operated and autonomous platforms with image and video sensors are being applied to new applications every day, e.g., wildland fire monitoring, search and rescue operations. Further, edge computing devices provide significant onboard computational capability, supporting an increasingly complex range of on-board autonomy and analytics. Combined with real-world wireless network limitations, there is an increasing interest in compressed video and image products. To reduce data volumes, a viable approach is to send a task description or query to the robotic platform. The platform can then publish a highly compressed image product back to an operator that is designed to answer the specific query. We develop a framework for evaluating compressed image products by focusing on their ability to support specific tasks or queries. We develop a task model based on Item-Response Theory and implement it as a multi-level Bayesian model, and we evaluate the utility of this model with an object classification task. We demonstrate the approach by comparing two different image compression methods using inexperienced users recruited with the Amazon Mechanical Turk (AMT) platform. The result is a potential reduction in file size from gigabytes to less than a few megabytes without loss in task performance.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years