Structured spatial domain image and data comparison metrics

Abstract

Often, images or datasets have to be compared, to facilitate choices of visualization and simulation parameters respectively. Common comparison techniques include side-by-side viewing and juxtaposition, in order to facilitate visual verification of verisimilitude. In this paper, we propose quantitative techniques which accentuate differences in images and datasets. The comparison is enabled through a collection of partial metrics which, essentially, measure the lack of correlation between the datasets or images being compared. That is, they attempt to expose and measure the extent of the inherent structures in the difference between images or datasets. Besides yielding numerical attributes, the metrics also produce images, which can visually highlight differences. Our metrics are simple to compute and operate in the spatial domain. We demonstrate the effectiveness of our metrics through examples for comparing images and datasets.

Visual Reconstructability as a Quality Metric for Flow Visualization

“…Image level comparison techniques compare the data as depicted on images. In [SWMJ99] a composite metric for comparing grid‐based datasets is proposed. Zhou et al [ZCW02] evaluate several metrics for image‐level comparison between videos of experiments and visualization results.…”

“…For example, a simple image difference between a streamline visualization and a LIC visualization cannot convey anything about the relative merits of the two visualization. Even using those more advanced image difference metrics in [ZCW02] and [SWMJ99] would not provide an adequate solution to this problem.…”

Moment Invariants for the Analysis of 2D Flow Fields

“…For scalar fields a quantitative comparison method on the data level has been proposed by Edelsbrunner et al [7]. Sahasrabudhe et al [18] defined metrics for comparing data in the spatial domain, which are usable for images and other data sets. Previous work on comparative visualization for flow fields includes the work of Pagendarm et al [16] and Verma and Pang [22], which is based on the comparison of stream and vortex lines.…”

Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization

“…Gaddipati et al [7] presented a wavelet-based metric which captures the change in images wrought by operators and the image synthesis algorithms. Sahasrabudhe et al [27] proposed a quantitative technique which accentuates differences in images and data sets through a collection of partial metrics. A study of different image comparison metrics, categorized into spatial domain, spatial-frequency domain, and perceptually-based metrics, was presented in [33].…”

“…These metrics have clear physical meanings and are simple to compute. However, they are usually not effective in predicting the quality of the rendered images due to the lack of correlation between data and image, as indicated in [7], [15], [22], [27], [30]. Image-based metrics focus on the ultimate images the user perceives, and strive to capture the quality loss in the rendered images introduced by rendering low resolution data.…”