Abstract: Artifact quantification is provided in volume rendering. Since the visual conspicuity of rendering artifacts strongly influences subjective assessments of image quality, quantitative metrics that accurately correlate with human visual perception may provide consistent values over a range of imaging conditions.

Abstract: A computer implemented method for applying a visual discrimination model for single image applications includes receiving an image, defining a plurality of regions of interest in the image including a first region located to contain a feature or object to be detected and a second region located to encompass a background, determining metrics for visibility of the feature or object, wherein the metrics are determined by generating channel Just-Noticeable Difference (JND) maps for the single image determining JND summary metrics for the plurality of regions, and determining a difference in JND metrics between the plurality of regions, and adjusting parameters of the JND metrics to increase a visibility of the feature or object in the image.

Abstract: A computer implemented method for applying a visual discrimination model for single image applications includes receiving an image, defining a plurality of regions of interest in the image including a first region located to contain a feature or object to be detected and a second region located to encompass a background, determining metrics for visibility of the feature or object, wherein the metrics are determined by generating channel Just-Noticeable Difference (JND) maps for the single image determining JND summary metrics for the plurality of regions, and determining a difference in JND metrics between the plurality of regions, and adjusting parameters of the JND metrics to increase a visibility of the feature or object in the image.

Abstract: Artifact quantification is provided in volume rendering. Since the visual conspicuity of rendering artifacts strongly influences subjective assessments of image quality, quantitative metrics that accurately correlate with human visual perception may provide consistent values over a range of imaging conditions.

Abstract: Perception-based visual quality metrics are used in volume rendering. A perception-based visual quality metric is measured from one or more three-dimensional representations. For example, people tend to notice edges, so a numeric value representing the noticeable edges is calculated. The perception-based metric is used for developing volume renderers, calibrating across different renderers, calibrating across different rendering platforms, determining rendering parameter values as a function of rendering speed, selecting rendering parameter values for a given situation, providing a range of rendering options associated with gradual perception changes, and/or combinations thereof. The perception-based visual quality metric provides a quantifiable representation of importance to the user for a given application, assisting optimization of volume rendering.