Effect of image compression for model and human observers in signal-known-statistically tasks

Miguel P. Eckstein; Binh Pham; Craig K. Abbey

doi:10.1117/12.462673

12 April 2002 Effect of image compression for model and human observers in signal-known-statistically tasks

Miguel P. Eckstein, Binh Pham, Craig K. Abbey

Proceedings Volume 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment; (2002) https://doi.org/10.1117/12.462673
Event: Medical Imaging 2002, 2002, San Diego, California, United States

Abstract

Previous studies have shown that model observers can be used for automated evaluation and optimization of image compression with respect to human visual performance in a task where the signal does not vary and is known a priori by the observer (signal known exactly, SKE). Here, we extend previous work to two tasks that are intended to more realistically represent the day-to-day visual diagnostic decision in the clinical setting. In the signal known exactly but variable task (SKEV), the signal varies from trial to trial (e.g., size, shape, etc) but is known to the observer. In the signal known statistically task (SKS) the signal varies from trial to trial and the observer does not have knowledge of which signal is present in that trial. We compare SKEV/SKS human and model observer performance detecting simulated arterial filling defects embedded in real coronary angiographic backgrounds in images that have undergone different amounts of JPEG and JPEG 2000 image compression. Our results show that both human and model performance at low compression ratios is better for the JPEG algorithm than the JPEG 2000 algorithm. Metrics of image quality such as the root mean square error (or the related peak signal to noise ratio) incorrectly predict a JPEG 2000 superiority. Results also show that although model and to a lesser extent human performance improves with the trial to trial knowledge of the signal present (SKEV vs. SKS task), conclusions about which compression algorithm is better (JPEG vs. JPEG 2000) for the current task would not change whether one used an SKEV or SKS task. These findings might suggest that the computationally more tractable SKEV models could be used as a good first approximation for automated evaluation of the more clinically realistic SKS task.

Citation Download Citation

Miguel P. Eckstein, Binh Pham, and Craig K. Abbey "Effect of image compression for model and human observers in signal-known-statistically tasks", Proc. SPIE 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, (12 April 2002); https://doi.org/10.1117/12.462673

ACCESS THE FULL ARTICLE

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