Encoding Visual Information Using Anisotropic Transformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image contrast enhancement via entropy production
Real-Time Imaging - Special issue on imaging in bioinformatics: Part III
Evaluation of Image Corrected by Retinex Method Based on S-CIELAB and Gazing Information
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
On the Use of Gaze Information and Saliency Maps for Measuring Perceptual Contrast
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
GAFFE: A Gaze-Attentive Fixation Finding Engine
IEEE Transactions on Image Processing
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Image quality assessment for the visually impaired
UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: user and context diversity - Volume 2
Hi-index | 0.00 |
In this paper we present a novel method to measure perceptual contrast in digital images. We start from a previous measure of contrast developed by Rizzi et al. [26], which presents a multilevel analysis. In the first part of the work the study is aimed mainly at investigating the contribution of the chromatic channels and whether a more complex neighborhood calculation can improve this previous measure of contrast. Following this, we analyze in detail the contribution of each level developing a weighted multilevel framework. Finally, we perform an investigation of Regions-of-Interest in combination with our measure of contrast. In order to evaluate the performance of our approach, we have carried out a psychophysical experiment in a controlled environment and performed extensive statistical tests. Results show an improvement in correlation between measured contrast and observers perceived contrast when the variance of the three color channels separately is used as weighting parameters for local contrast maps. Using Regions-of-Interest as weighting maps does not improve the ability of contrast measures to predict perceived contrast in digital images. This suggests that Regions-of-Interest cannot be used to improve contrast measures, as contrast is an intrinsic factor and it is judged by the global impression of the image. This indicates that further work on contrast measures should account for the global impression of the image while preserving the local information.