Secondary quantization of gray-level images for minimum visual distortion

James R. Sullivan; Lawrence A. Ray

doi:10.1117/12.135954

27 August 1992 Secondary quantization of gray-level images for minimum visual distortion

James R. Sullivan, Lawrence A. Ray

Proceedings Volume 1666, Human Vision, Visual Processing, and Digital Display III; (1992) https://doi.org/10.1117/12.135954
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

Abstract

In digital imaging systems it is often necessary to reduce the bit precision of an image due to limitations in transmission or storage. The 8 bit storage of a 12 bit medical image is a good example. Generally, the input images are linearly quantized, and the goal is to find the fixed, digital mapping that preserves the highest image quality at the reduced bit precision. Since the response of the visual system to brightness differences is nonlinear, the optimal mapping is nonlinear. The traditional approach is to use one of the commonly accepted models of the visual system, e.g. a logarithm or power-law, to construct a Look-Up-Table (LUT) that performs the digital mapping. This paper will demonstrate that this approach is visually suboptimal for finite input precision, even if the visual model is perfect. A better method for constructing the digital mapping or LUT will be derived by posing the problem as a combinational optimization problem of taking N bits from M bits, where N is less than M, such that a visual distortion metric is minimized. Computer generated images will be used to demonstrate the method in a 12 bit to 8 bit application, and a 6 bit to 2 bit example will be included to illustrate its convergence characteristics.

Citation Download Citation

James R. Sullivan and Lawrence A. Ray "Secondary quantization of gray-level images for minimum visual distortion", Proc. SPIE 1666, Human Vision, Visual Processing, and Digital Display III, (27 August 1992); https://doi.org/10.1117/12.135954

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