Abstract
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.
Highlights
Since the ’70s, society is witnessing a rapid evolution of digital imaging devices
Building on the theories of local measures developed in these last decades, we present a novel measure of contrast for digital color images named Weighted–Level Framework (WLF), which is based on three mathematical aspects: multilevel analysis, Difference of Gaussians model and variance weighting
A pyramidal structure is created halving the image at each iteration with prefiltering in order to avoid aliasing at low resolutions
Summary
Since the ’70s, society is witnessing a rapid evolution of digital imaging devices (e.g., cameras, high definition TVs, 3D monitors and laser printers). Measuring the difference between the darkest and lightest point in an image does not predict perceived contrast since perceived contrast is influenced by the surround (viewing conditions) and the spatial arrangement of the image. Measuring the difference between the darkest and lightest point in an image does not predict perceived contrast since perceived contrast is influenced by the surround and the spatial arrangement of the image Parameters such as resolution, viewing distance, lighting conditions, image content, memory color might affect how observers perceive contrast. In order to deal with this locality in image appearance, different image processing methods and frameworks have been developed with the intent to exhibit behaviors similar to the HVS, such as Automatic Color Equalization (ACE), Spatio-Temporal Retinex-inspired (STRESS), image Color Appearance Model (iCAM), and its evolutions, and the various Retinex implementations, which are the interest of this work.
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