Generation and Evaluation of Power Model for Mobile AMOLED Display Using RGB Color Space Partitioning Method Considering Power


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 9, pp. 335-344, Sep. 2018
10.3745/KTSDE.2018.7.9.335, Full Text:
Keywords: Power Model, Model Evaluation, Model Generationg, AMOLED
Abstract

The power model is needed to handle the power consumption of mobile AMOLED display at the software level. However, the existing studies to generate the power model have required the experimental environment and equipment for the power measurement activity. In addition, the combination of RGB values used for modeling was imprudent and small, so it was difficult to reflect the mutual influence between the RGB values into the model. To solve these problems, we propose an RGB color space partitioning method, which is used to prudently sample the combinations of the RGB values based on the color or the power. We also propose a process for generating a mapping table composed of . We analyzed the characteristics of the samples generated according to the proposed partitioning methods, taking into account the color and the power, and generated the mapping table for the AMOLED display. Furthermore, we confirmed the reusability of the mapping table by utilizing one mapping table multiple times in evaluating different power models. These mapping tables are provided to researchers and can be used to generate and evaluate power models without power measurement activities.


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Cite this article
[IEEE Style]
D. Baek, Y. Choi, B. Lee and J. Lee, "Generation and Evaluation of Power Model for Mobile AMOLED Display Using RGB Color Space Partitioning Method Considering Power," KIPS Transactions on Software and Data Engineering, vol. 7, no. 9, pp. 335-344, 2018. DOI: 10.3745/KTSDE.2018.7.9.335.

[ACM Style]
Dusan Baek, Yoo-Rim Choi, Byungjeong Lee, and Jung-Won Lee. 2018. Generation and Evaluation of Power Model for Mobile AMOLED Display Using RGB Color Space Partitioning Method Considering Power. KIPS Transactions on Software and Data Engineering, 7, 9, (2018), 335-344. DOI: 10.3745/KTSDE.2018.7.9.335.