Abstract Different quality metrics have been proposed in the literature to evaluate how well a visualization represents the underlying data. In this paper, we present a new information‐theoretic framework that quantifies the information transfer between the source data set and the rendered image. This approach is based on the definition of an observation channel whose input and output are given by the intensity values of the volumetric data set and the pixel colors, respectively. From this channel, the mutual information, a measure of information transfer or correlation between the input and the output, is used as a metric to evaluate the visualization quality. The usefulness of the proposed observation channel is illustrated with three fundamental visualization applications: selection of informative viewpoints, transfer function design, and light positioning.

In this paper, we present a new framework for multimodal volume visualization that combines several information-theoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input datasets, and afterward, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1-D transfer functions. To obtain the opacity, we apply an optimization process that minimizes the informational divergence between the visibility distribution captured by a set of viewpoints and a target distribution proposed by the user. This distribution is defined either from the dataset features, from manually set importances, or from both. Other problems related to the multimodal visualization, such as the computation of the fused gradient and the histogram binning, have also been solved using new information-theoretic strategies. The quality and performance of our approach are evaluated on different datasets.

Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel created between the two input data sets that permit us to quantify the information associated with each intensity value. This specific information is obtained from three different ways of decomposing the mutual information of the channel. In addition, an assessment criterion based on the information content of the fused data set can be used to analyze and modify the initial selection of the voxels by weighting the contribution of each data set to the final result. The proposed approach has been integrated in a general framework that allows for the exploration of volumetric data models and the interactive change of some parameters of the fused data set. The proposed approach has been evaluated on different medical data sets with very promising results.