Visual Multiplexing

Abstract

The majority of display devices used in visualization are 2D displays. Inevitably, it is often necessary to overlay one piece of visual information on top of another, especially in applications such as multi‐field visualization and geo‐spatial information visualization. In this paper, we present a conceptual framework for studying the mechanisms for overlaying multiple pieces of visual information while allowing users to recover occluded information. We adopt the term ‘multiplexing’ from tele‐ and data communication to encompass all such overlapping mechanisms. We establish 10 categories of visual multiplexing mechanisms. We draw support evidence from both perception literature and existing works in visualization to support this conceptual framework. We examine the relationships between multiplexing and information theoretic measures. This new conceptual categorization provides the much‐needed theory of visualization with an integral component.

Quality Metrics for Information Visualization

“…They built on the initial work by Yang‐Peláez et al [YPF00] and proposed a number of entropy‐based measures, including visual‐mapping ratio, information loss ratio, and display space utilization; these measures are akin to the data‐ink ratio [TGM83]. Chen et al also discussed visual multiplexing [CWB*14] in relation to the information theoretic measures. They describe various mechanisms for overlaying multivariate data and discuss how to overcome perceptual difficulties such as occlusion and cluttering that arise from the interference among spatially overlapping visual channels.…”

What May Visualization Processes Optimize?

“…In developing theories of visualization, much effort has been made in formulating categorizations and taxonomies (e.g., [3,60,73]). Some 25 different proposals are listed in [14,15]. In addition, a number of conceptual models have been proposed, including object-oriented model by Silver [55], feature extraction and representation by van Walsum et al [67], visualization exploration by Jankun-Kelly et al [38], distributed cognition model by Liu et al [43], predictive data-centered theory by Purchase et al [48], Visualization Transform Design Model by Purchase et al [48], cognition model for visual analytics by Green et al [30], sensemaking and model steering by Endert et al [21], modelling visualization using semiotics and category theory by Vickers et al [65], composition of visualization tasks by Brehmer and Munzner [9], and visual embedding by Demiralp et al [20].…”

“…This holistic nature of information-theoretic reasoning has enabled many applications in visualization, including light source placement by Gumhold [33], view selection in mesh rendering by Vázquez et al [64] and Feixas et al [22], view selection in volume rendering by Bordoloi and Shen [5], and Takahashi and Takeshima [56], focus of attention in volume rendering by Viola et al [66], multi-resolution volume visualization by Wang and Shen [68], feature highlighting in unsteady multi-field visualization by Jänicke and Scheuermann [35,37], feature highlighting in time-varying volume visualization by Wang et al [70], transfer function design by Bruckner and Möller [10], and by Ruiz et al [8,49], multimodal data fusion by Bramon et al [6], evaluating isosurfaces [74], measuring of observation capacity [7], measuring information content in multivariate data [23], and confirming the mathematical feasibility of visual multiplexing [15].…”

Uncertainty Treemaps

“…Uncertainty visualization is a broad field, ranging from data acquisition to mapping to representation, and reasoning about uncertainty [8,21]. We consider uncertainty visualization to be a process of multiplexing certain and uncertain data in such a way that humans can successfully demultiplex it [10,19] and reason about it [16,31]. Within this information-theoretical consideration, we can distinguish between encoder, channel, and decoder.…”