Abstract
Spatial filtering, which aims to mimic the contrast sensitiv- ity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate impercep- tible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the param- eters, which are designed to be adaptive to the corresponding view- ing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were con- ducted to evaluate the performance of our approach. The experimen- tal sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharp- ness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model. © 2012 SPIE and IS&T. (DOI: 10.1117/1.JEI.21.2.023021)
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