Visual masking is an effect that contents of the image reduce the detectability of a given target signal hidden in the image. The effect of visual masking has found its application in numerous image processing and vision tasks. In the past few decades, numerous research has been conducted on visual masking based on models optimized for artificial targets placed upon unnatural masks. Over the years, there is a tendency to apply masking model to predict natural image quality and detection threshold of distortion presented in natural images. However, to our knowledge few studies have been conducted to understand the generalizability of masking model to different types of distortion presented in natural images. In this work, we measure the ability of natural image patches in masking three different types of distortion, and analyse the performance of conventional gain control model in predicting the distortion detection threshold. We then propose a new masking model, where detail loss and additive defects are modeled in two parallel vision channels and interact with each other via a cross masking mechanism. We show that the proposed cross masking model has better adaptability to various image structures and distortions in natural scenes.

Date Published: 17 March 2015
PDF: 14 pages
Proc. SPIE 9394, Human Vision and Electronic Imaging XX, 93941D (17 March 2015); doi: 10.1117/12.2083476

Published in SPIE Proceedings Vol. 9394:
Human Vision and Electronic Imaging XX
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder, Editor(s)