Masking of Gaussian Noise in Color Images: A Psychophysical Study of Just-Noticeable Differences Using Synthetic Image Patches of Different Luminance Value

Luis Miguel Calvo, Pedro Latorre-Carmona, Samuel Morillas, Rafael Huertas, Rafal Mantiuk

Abstract: Understanding how noise affects visual image quality and modeling its perception are crucial aspects of digital image processing, especially when image quality is of paramount importance. Taking into account that the most common sources of camera noise are modelled in the linear RGB space (lRGB) as white Gaussian noise, we focus on this particular noise type. In our work, we are particularly interested in studying how different noise intensities are perceived. We assume that a good indicator for this perception is the determination of just noticeable differences (JNDs) among noise intensities. We also assume that there is a dependency between the JNDs, the noise intensity and the background luminance. Psychophysical experiments show that there is a dependency on the computed JNDs that is related to reference image noise intensity in the Weber law. The relationship between the JND values and the background luminance is, however, not so evident. These results highlight the fact that JNDs of Gaussian noise in images present interdependencies worth to be further studied through more experiments in the near future.

External IDs:dblp:conf/ibpria/CalvoLMHM25