“Classified Texture Resizing for Mobile Devices”

  • ©Jae-Ho Nah, Byeongjun Choi, and Yeongkyu Lim

  • ©Jae-Ho Nah, Byeongjun Choi, and Yeongkyu Lim

  • ©Jae-Ho Nah, Byeongjun Choi, and Yeongkyu Lim



Entry Number: 75


    Classified Texture Resizing for Mobile Devices



    Power consumption is one of the most important factors in mobile computing. Especially for high-quality games, it takes a lot of computing power to render visual effects. In order to reduce this, some rendering techniques (e.g., Samsung Game Tuner) adjust rendering parameters (screen resolution, frame rates, and texture sizes) to improve power efficiency or performance. Among them, the texture resizing reduces power consumption in some cases, but it sometimes results in poor rendering quality or no energy saving.

    To improve the texture resizing, we present the classified texture resizing technique. Our main idea is to classify textures into certain types and to apply a different approach to each type. As a result, our approach minimizes degradation of rendering quality and can be applied to wider applications. Our experimental results show up to 16% power reduction of a GPU and DRAM.


    Sparsh Mittal and Jeffrey S Vetter. 2015. A survey of methods for analyzing and improving GPU energy efficiency. ACM Computing Surveys (CSUR) 47, 2 (2015), 19.
    Samsung Electronics. 2016. Game Tuner. https://play.google.com/store/apps/details?id=com.samsung.android.gametuner.thin. (2016).
    Bartosz Taudul. 2016. etcpak 0.5. https://bitbucket.org/wolfpld/etcpak. (2016).



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