臺灣博碩士論文加值系統

近年來由於科技的發展，行動裝置已經成為人們生活中不可或缺的物品，任何常見的娛樂或文書功能只要使用適當的應用程式都在行動裝置執行。但行動裝置的持續使用時間仍然是一個很大的問題，目前行動裝置主要消耗最多能量的部分由大而小分別為螢幕、無線網路與計算核心，有鑑於此，我們針對螢幕進行了省電的研究。在螢幕的各部位中，最為耗電的部分就是背光模組，因此在本篇論文中，我們提出了一個根據主觀評量分數對於背光面板亮度進行調整的演算法。首先，為了取得人眼視覺的基礎資料，我們進行一系列的主觀評量測試，並根據測試結果，將顯示畫面亮度與對比度納入考量，設計出一個簡單有效率的調整演算法，最後，為了取得在省電與視覺品質取得平衡，我們在該演算法中使用了不同參數並再度進行了主觀評量測試，並得出了針對該裝置的最佳調整法。實驗結果顯示此調整演算法在單張影像上能夠得到不錯的省電與視覺平衡效果。

In the last decade, mobile devices had been an important part of our life. With the advance in information and communication technology, mobile devices can achieve almost everything with proper apps. However, the durability of the mobile devices is still a problem. The major parts which consume most of the energy are the screen, communication unit and the computation unit arranged in descending order. Base on this fact, we focused on lower the power consumption of the screen by adjusting the backlight level, since the backlight system dominate the screen power requirement. So we proposed a backlight adjustments based on the subjective quality assessment. First of all, for collecting the basic data of human visual perception, series of quality assessment are performed. According to these data, this paper designed a backlight adjustment algorithm based on the luminance and contrast. In the end, for the balance between visual quality and energy saving, this algorithm has been assigned different parameters and performed the subjective quality assessment again. Experimental results demonstrate that the proposed algorithm could achieve better energy cost while preserving the visual quality in single image display.

中文摘要 i
Abstract ii
Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Overview of power saving on mobile devices 1
1.2 Motivation 3
1.3 Contribution 4
1.4 Organization of the Thesis 6
Chapter 2 Background review 7
2.1 TFT-LCD Architecture 8
2.2 Backlight Scaling 11
2.3 Mobile Streaming Backlight scaling optimization 13
2.4 Content based digital power saving methods 16
2.5 Subjective quality assessment 17
Chapter 3 Subjective Quality Assessment For Human Visual Perception 18
3.1 Adopted quality assessment method 19
3.2 Experiment environment 21
3.3 Experiment equipment 21
3.4 Image quality assessment base on brightness 22
3.5 Image quality assessment base on contrast 24
3.6 Image quality assessment base on complexity 25
3.7 Video quality assessment base on motion 27
3.8 Proper display backlight experiment 29
Chapter 4 Proposed Backlight Adjustment Algorithm 32
Chapter 5 Experiment result 37
Chapter 6 Conclusions and Future Works 42
Reference 44

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[3]P.-C. Hsiu, C.-H. Lin, and C.-K. Hsieh, “Dynamic backlight scaling optimization: a cloud-based energy-saving service for mobile streaming applications,” accepted and to appear in IEEE Transactions on Computers, 2012.
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[6]ITU-T Rec. P.910, “Subjective video quality assessment methods for multimedia applications,” April 2008.
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[8]W.-C. Cheng and C.-F. Chao, “Minimization for LED-backlit TFT-LCDs,” in Proceedings of 43rd ACM/IEEE of Design Automation Conference, pp.608-611, 2006.
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[11]P.-C. Hsiu, C.-H. Lin, and C.-K. Hsieh, “Dynamic backlight scaling optimization: a cloud-based energy-saving service for mobile streaming applications,” accepted and to appear in IEEE Transactions on Computers, 2012.