Perceptually optimal color reproduction What requirements do people place on optimal color reproduction of reallife scenes? We suggest that when people look at images containing familiar categories of objects, two primary factors shape their subjective impression of how optimal colors are reproduced: perceived naturalness and perceived colorfulness of the images. To test the model subjects were asked to evaluate the perceived naturalnessu0027, colorfulnessu0027 and qualityu0027 of images of reallife scenes. The judgments were related to statistical parameters of the color point distribution over the images in CIE 1976 (L*u*v*) color space. It was found that the perceptually optimal color reproduction can be derived from this statistic within the framework of our model. We also specify the naturalness, colorfulness and quality indices, describing the observeru0027s judgments.

A Deeper Look at Human Visual Perception of Images How would one describe an image? Interesting? Pleasant? Aesthetic? A number of studies have classified images with respect to these attributes. A common approach is to link lower level image features with higher level properties, and train a computational model to perform classification using humanannotated ground truth. Although these studies generate algorithms with reasonable prediction performance, they provide few insights into why and how the algorithms work. The current study focuses on how multiple visual factors affect human perception of digital images. We extend an existing dataset with quantitative measures for human perception of 31 image attributes under 6 different viewing conditions: images that are intact, inverted, grayscale, inverted and grayscale, and images showing mainly low or highspatial frequency information. Statistical analyses indicate varying importance of holistic cues, color information, semantics, and saliency on different types of attributes. Building on these insights we build an empirical model of human image perception. Motivated by the empirical model, we designed computational models that predict highlevel image attributes. Extensive experiments demonstrate that understanding human visual perception helps create better computational models.