Optimized error diffusion based on a human visual model

Bernd W. Kolpatzik; Charles A. Bouman

doi:10.1117/12.135964

27 August 1992 Optimized error diffusion based on a human visual model

Bernd W. Kolpatzik, Charles A. Bouman

Proceedings Volume 1666, Human Vision, Visual Processing, and Digital Display III; (1992) https://doi.org/10.1117/12.135964
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

Abstract

Error diffusion is a powerful means to improve the subjective quality of a quantized image by shaping the spectrum of the display error. Considering an image in raster ordering, this is done by adding a weighted sum of previous quantization errors to the current pixel before quantization. These weights form an error diffusion filter. In this paper a method is proposed to find an optimized error diffusion filter for image display applications. The design is based on the lowpass characteristic of the contrast sensitivity of the human visual system. The filter is chosen so that a cascade of the quantization system and the observer's visual modulation transfer function yields a whitened spectrum of error. It is shown in this paper that the optimal error diffusion filter corresponds to a linear prediction filter of the human visual transfer function. A first order linear filter for an underlying non-separable vision model is examined. The resulting images contain mostly high frequency components of the display error, which are less noticeable for the viewer. This corresponds well to previously published results about the visibility of halftoning patterns. An informal comparison with other error diffusion algorithms shows less artificial contouring and increased image quality.

Citation Download Citation

Bernd W. Kolpatzik and Charles A. Bouman "Optimized error diffusion based on a human visual model", Proc. SPIE 1666, Human Vision, Visual Processing, and Digital Display III, (27 August 1992); https://doi.org/10.1117/12.135964

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