A Data-Driven Approach to Hue-Preserving Color-Blending

Abstract

Abstract-Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced methods have also been proposed, but the problem is still far from being solved. Here we present an alternative to these existing methods specifically devised to avoid false colors and preserve visual depth ordering. Our approach is data driven and follows the recently formulated knowledge-assisted visualization (KAV) paradigm. Preference data, that have been gathered in web-based user surveys, are used to train a support-vector machine model for automatically predicting an optimized hue-preserving blending. We have applied the resulting model to both volume rendering and a specific information visualization technique, illustrative parallel coordinate plots. Comparative renderings show a significant improvement over previous approaches in the sense that false colors are completely removed and important properties such as depth ordering and blending vividness are better preserved. Due to the generality of the defined data-driven blending operator, it can be easily integrated also into other visualization frameworks.

Pondering the Concept of Abstraction in (Illustrative) Visualization

“…7), inspired by related work 2. So far, illustrative visualization techniques have largely been created for spatial data (like our focus in this article)-only few examples for the illustrative visualization of "abstract data" exist (e. g., [4], [41], [59], [68], [73], [83], [111], [114], [116]; also see Fig. 9).…”

“…8). Such bundled/clustered and thus simplified representations can also be found for representations of non-spatial data such as parallel coordinates [59], [73], [83], [114] (Fig. 9).…”

Multivariate spatial data visualization: a survey

“…Interactive classification [65] [67], [19], [18], [40], [39], [68], [41] Data mining [26], [66], [50], [63], [60], [62] Topological structures [43], [12], [6], [16], [7], [64], [54] Fusion visualization Data fusion [53], [19], [32], [28], [7], [24], [41], [13] Feature fusion [38], [18], [11] Image fusion [4], [66], [49] Correlation analysis Voxels [44], [63] Variables [58], [3], [13] Numerical values [22], [27], [6], [39] Features [47], [59] Value-variable [3], [39] 2 Feature Classification…”

Learning Natural Colors for Image Recoloring

“…One way to overcome this limitation is to use a set of reference image colorings to learn implicit rules about suitable image color combinations. The idea of utilizing large database has been proven effective on media manipulation, synthesis and understanding [KGZ*12, HCX*13]. Recently, Lin et al gave a data driven method for colorization of patterns [LRFH13], taking into account the spatial correlation of colored regions in the pattern.…”