隨著科技的進步，我們生活周遭處處可見影像顯示裝置，如手機、電腦、電視等。由於顯示裝置大小不盡相同，故影像呈現在裝置上時，常常會用到影像縮放技術，例如在手機上觀看大尺寸照片時，整張照片會等比例縮小呈現，這會使得影像中的主體變得很小，讓人在觀看時造成不便。在本篇論文中，我們提出一個有效率且高品質的智慧型內容感知之影像縮放系統，此系統會先分析影像中的主體區域，再對整張影像做智慧型壓縮，其影像壓縮後仍能完整保留畫面中的主體，使主體更加突出明顯以達到一個很好的視覺效果，同時也可以減少影像的資料量。此技術可應用在各種尺寸顯示裝置上，也可應用在影像傳輸(H.264/AVC)上，可有效降低影像的資料量，不論對影像壓縮或視訊編碼都有實質上的貢獻。

Along with the advancement of technology, image display devices such as mobile phones, computers and televisions are ubiquitous everywhere in our lives. Due to the different sizes of display devices, digital image scaling technology is often used on the devices while presenting images. For example, when large-size photos are viewed on mobile phones, they tend to present as scaled-down images of the entire pictures, making the main subjects quite small and thus causing inconvenience for viewers. On this account, this study has offered an efficient and high-quality intelligent content-aware image resizing system to solve this problem. This system will first analyze the main area of the image, and then applies an intelligent compression process on the entire image. By doing this, images can still have a complete main subject even after being compressed, not only achieving an excellent visual effect while making the main subject more prominent and obvious, but also reducing the data volume of images. Except for various sizes of display devices, this technology can also be applied on video transmission (H.264/AVC) to effectively reduce the data volume of images, making a substantial contribution to both the image scaling and video coding.

中文摘要 i
Abstract ii
Contents iii
List of Figures iv
List of Tables v
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Motivation 4
1.3 Contribution 5
1.4 Organization 6
Chapter 2 Background Review 7
2.1 Overview of Conventional Image Resizing 7
2.2 Overview of Energy Function 9
2.3 Seams Carving Algorithm 11
Chapter 3 Proposed Robust Detection Model for Content Aware Image Resizing 16
3.1 Overview 16
3.2 Analysis of Visual Attentions 19
3.3 Proposed Energy map 22
3.4 Robust important area detection 26
3.5 Iteration 33
Chapter 4 Experimental Results 37
4.1 Important Area Detection 37
4.2 Intellingent image resizing 45
Chapter 5 Conclusions and Future Works 50
5.1 Conclusion and Future Work 50
Reference

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