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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 郭大維 | |
| dc.contributor.author | Yu-Chuan Chang | en |
| dc.contributor.author | 張育銓 | zh_TW |
| dc.date.accessioned | 2021-07-11T15:41:15Z | - |
| dc.date.available | 2023-08-21 | |
| dc.date.copyright | 2018-08-21 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-13 | |
| dc.identifier.citation | [1] Most popular Apple App Store categories in May 2018. https://www.statista.com/statistics/270291/popular-categories-in-the-app-store/.
[2] Y. Bababekova, M. Rosenfield, J. E. Hue, and R. R. Huang. Font Size and Viewing Distance of Handheld Smart Phones. Optometry & Vision Science, 88(7):795–797, 2011. [3] C. Hwang, S. Pushp, C. Koh, J. Yoon, Y. Liu, S. Choi, and J. Song. RAVEN: Perception-aware Optimization of Power Consumption for Mobile Games. In Proc. of ACM MobiCom, pages 422–434, 2017. [4] W. M. Chen, S. W. Cheng, and P. C. Hsiu. A user-centric cpu-gpu governing framework for 3d mobile games. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pages 1–1, 2018. [5] X. Chen, J. Zheng, Y. Chen, M. Zhao, and C. J. Xue. Quality-retaining oled dynamic voltage scaling for video streaming applications on mobile devices. In DAC Design Automation Conference 2012, pages 1000–1005, June 2012. [6] L. N. Huynh, Y. Lee, and R. K. Balan. Deepmon: Mobile gpu-based deep learning framework for continuous vision applications. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys’17, pages 82–95, 2017. [7] P. Kellnhofer, T. Ritschel, K. Myszkowski, and H.-P. Seidel. Transformation-aware Perceptual Image Metric. Journal of Electronic Imaging, 25(5):053014, 2016. [8] D. Kim, N. Jung, and H. Cha. Content-centric Display Energy Management for Mobile Devices. In Proc. of IEEE/ACM DAC, pages 41:1–41:6, 2014. [9] D. Kim, N. Jung, Y. Chon, and H. Cha. Content-centric energy management of mobile displays. IEEE Transactions on Mobile Computing, 15(8):1925–1938, 2016. [10] C. H. Lin, C.-K. Kang, and P. C. Hsiu. Catch your attention: Quality-retaining power saving on mobile oled displays. In 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC), pages 1–6, 2014. [11] T. S. Ou, Y. H. Huang, and H. H. Chen. Ssim-based perceptual rate control for video coding. IEEE Transactions on Circuits and Systems for Video Technology, 21(5): 682–691, 2011. [12] B. Ratner. The correlation coefficient: Its values range between +1/-1, or do they? Journal of Targeting, Measurement and Analysis for Marketing, 17(2):139–142, Jun 2009. [13] H. R. Sheikh, M. F. Sabir, and A. C. Bovik. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11):3440–3451, 2006. [14] S. Shi, C.-H. Hsu, K. Nahrstedt, and R. Campbell. Using graphics rendering contexts to enhance the real-time video coding for mobile cloud gaming. In Proceedings of the 19th ACM International Conference on Multimedia, pages 103-112,2011. [15] S. Wang, A. Rehman, Z. Wang, S. Ma, and W. Gao. Perceptual video coding based on ssim-inspired divisive normalization. IEEE Transactions on Image Processing, 22(4):1418–1429, 2013. [16] Z. Wang, A. C. Bovik, and L. Lu. Why is image quality assessment so difficult? In 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 4, pages IV–3313–IV–3316, May 2002. [17] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, April 2004. [18] S. Zeki. A Vision of the Brain. Blackwell Scientific Publications, 1993. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79067 | - |
| dc.description.abstract | Content similarity measurement enables mobile applications to reduce unnecessary computation energy without compromising user visual experience. Existing measures assess the similarity between image frames based on pixels, resulting in non-negligible overhead. This paper presents a lightweight similarity measure called LSIM, which assesses content similarity based on objects in graphics applications. To evaluate the efficacy, we implement LSIM in Android and conduct extensive experiments on a commercial smartphone with various mobile gaming applications. The results show that LSIM is highly correlated with a commonly used pixel-wise measure while incurring nearly zero overhead. We also apply LSIM to a CPU-GPU governing framework to mitigate the rendering of similar frames, thereby reducing the energy consumption by up to 27.3% while maintaining satisfactory visual quality. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T15:41:15Z (GMT). No. of bitstreams: 1 ntu-107-R05922057-1.pdf: 4205321 bytes, checksum: ff02f2f24e39749b1f77117c0b114f2e (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
中文摘要ii Abstract iii Contents iv List of Figures vi List of Tables vii 1 Introduction 1 2 Background and Motivation 4 2.1 Graphics Rendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Motivational Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Lightweight Content Similarity Assessment 7 3.1 Design Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 Transformation Matrix Decomposition . . . . . . . . . . . . . . . . . . . 8 3.2.1 Object Position . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.2 Object Orientation . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.3 Object Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 Similarity Score Calculation . . . . . . . . . . . . . . . . . . . . . . . . 14 3.4 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4 Performance Evaluation 18 4.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 Validation of LSIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.1 Overhead Measurement . . . . . . . . . . . . . . . . . . . . . . 20 4.2.2 Correlation with SSIM . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Efficacy of LSIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3.1 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.2 Visual Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5 Concluding Remarks 26 Bibliography 27 | |
| dc.language.iso | en | |
| dc.subject | 圖形密集型應用程式 | zh_TW |
| dc.subject | 內容相似度評估 | zh_TW |
| dc.subject | 手機系統 | zh_TW |
| dc.subject | Content Similarity Assessment | en |
| dc.subject | Mobile System | en |
| dc.subject | Graphics-intensive Applications | en |
| dc.title | 針對圖形密集型應用之輕量級影像相似度評估 | zh_TW |
| dc.title | Lightweight Content Similarity Assessment for Mobile Graphics Applications | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 修丕承 | |
| dc.contributor.oralexamcommittee | 劉邦鋒,洪士灝,王克中 | |
| dc.subject.keyword | 手機系統,圖形密集型應用程式,內容相似度評估, | zh_TW |
| dc.subject.keyword | Mobile System,Graphics-intensive Applications,Content Similarity Assessment, | en |
| dc.relation.page | 29 | |
| dc.identifier.doi | 10.6342/NTU201803089 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-08-21 | - |
| Appears in Collections: | 資訊工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-107-R05922057-1.pdf Restricted Access | 4.11 MB | Adobe PDF |
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