GreenVis: Energy-Saving Color Schemes for Sequential Data Visualization on OLED Displays

The organic light emitting diode (OLED) display has recently become popular in the consumer electronics market. Compared with current LCD display technology, OLED is an emerging display technology that emits light by the pixels themselves and doesn’t need an external back light as the illumination source. In this paper, we offer an approach to reduce power consumption on OLED displays for sequential data visualization. First, we create a multi-objective optimization approach to find the most energy-saving color scheme for given visual perception difference levels. Second, we apply the model in two situations: pre-designed color schemes and auto generated color schemes. Third, our experiment results show that the energy-saving sequential color scheme can reduce power consumption by 17.2% for pre-designed color schemes. For auto-generated color schemes, it can save 21.9% of energy in comparison to the reference color scheme for sequential data

Categorical Colormap Optimization with Visualization Case Studies

—Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital

Categorical colormap optimization with visualization case studies

Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital

Energy Accounting and Optimization for Mobile Systems

Energy accounting determines how much a software process contributes to the total system energy consumption. It is the foundation for evaluating software and has been widely used by operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track every hardware use by software in a heterogeneous multicore system like modern smartphones and tablets. This work provides the ground truth for energy accounting based on multi-player game theory and offers the first evaluation of existing energy accounting policies, revealing their important flaws. The proposed ground truth is based on Shapley value, a single value solution to multi-player games of which four axiomatic properties are natural and self-evident to energy accounting. This work further provides a utility optimization formulation of energy management and shows, surprisingly, that energy accounting does not matter for existing energy management solutions that control the energy use of a process by giving it an energy budget, or budget based energy management (BEM). This work shows an optimal energy management (OEM) framework can always outperform BEM. While OEM does not require any form of energy accounting, it is related to Shapley value in that both require the system energy consumption for all possible combination of processes under question. This work reports a prototype implementation of both Shapley value-based energy accounting and OEM based scheduling. Using this prototype and smartphone workload, this work experimentally demonstrates how erroneous existing energy accounting policies can be, show that existing BEM solutions are unnecessarily complicated yet underperforming by 20% compared to OEM