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研究生: 鄭凱仁
Kai-Ren Jheng
論文名稱: 基於前景飽和度的HDR影像偏好評估
HDR Image Preferences Evaluation Based on Foreground Saturation
指導教授: 施國琛
Timothy K. Shih
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 120
中文關鍵詞: 高動態範圍影像影像品質評估前景像素HDR 影像評估
外文關鍵詞: Subjective Preferences, Foreground Pixel, HDR image evaluation
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  • 在過去,拍好一張照片需要很多的技巧與很好的設備,幸好現代科技能夠輕易地幫我們解決這些事情。如果因為錯誤的相機參數設定,拍了一張不理想的照片,照片本身可能會顯得沒有對比且顏色不飽和。影像增強的方法可以幫助我們解決這樣的窘境,使得照片回復他們該有的對比跟顏色。然而,影像增強不是萬能的,如果照片太多過曝或是過暗的區域,造成原因通常是相機的硬體限制,這樣的影像增強是救不回來的。所以我們需要高動態範圍(High Dynamic Range)影像技術來彌補這樣的情形。
    有很多種方法可以產生一張HDR影像,大致上分為兩種,一種為tone reproduction,另外一種為exposure fusion,但較少的論文會去探討產生出來的HDR影像,跟人們對照片的喜好關係,換句話說,比較少的論文會去探討人們喜歡那種類型的HDR影像。在此篇論文中,我們用前景像素,找出HDR影像中的細節資訊,找到有細節資訊的區域後,再整合用色彩評估,去評估HDR影像。我們也設計了一些問卷,用了兩種方法,多選一、二選一,調查人們對HDR影像的喜好,再論文的後半部,相關係數實驗驗證了我們的方法與調查問卷的結果,我們的方法產生的指標,與調查的結果具有高度的相關性,換句話說,我們的指標可以預測人們對HDR影像的喜好。


    Shooting a good picture requires a lots of photography skills and good equipment. Thankfully, modern technology can make up for non-professionals. If someone taking a picture using the wrong exposure setting or the camera is not good enough to reproduce the scene, the photo produced would be in low contrast and no color. Thankfully, image enhancement technique can retrieve the lost detail and color information. However, image enhancement cannot deal with image that has lost almost all detail information. HDR technique is a solution to this kind of problem.
    There are many ways to generate a HDR image. They are mainly about how to do tone reproduction or how to do the exposure fusion. Others are focus on removing ghost effect. However, there are rarely studies which are related to people's feeling and the preferences to the HDR images. In other words, we have few research about what kinds of images (HDR images) are the good images depend on subjective feeling while we have many ways to generate all kinds of different HDR images. Hence, in this paper, we want to find out whether there is an index can reflect subjective feeling to HDR image. The method we proposed is call S_fpg. The foreground pixel used in image enhancement to evaluate the performance is used to find out the detail region of a HDR image because image enhancement and HDR are basically the same thing-retrieve the detail information from scene to image. Once finding out the foreground, the saturation measure is evolved to see the colorfulness of HDR image.

    A subjective study about HDR images preferences survey is conducted using online survey system. We conduct two kinds of survey to get the preferences data. One is choose-the-best-one and the other is two alternative forced choice. The reason we do choose-the-best-one test is that the conventional mean opinion score survey cause too much cognitive load to the testers. Furthermore, the score testers graded doesn’t have a standard. Ranking the preference image is another method. However, the testers have difficulty in ranking the image that they don’t like. Hence, we change the survey method to choose-the-best-one which significantly reduce the cognitive load to testers. Two alternative forced choice is suggested to be more intuitive and hence, we conduct another survey using 2AFC.
    Both of two results are consist with our index. The index is validated by correlation coefficient with subjective test using Spearman's rank correlation coefficient and Pearson correlation coefficient. The results show that our method is highly correlated with subjective preferences comparing with other objective measure metrics.

    Abstract I Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Thesis Organization 3 Chapter 2. Related Work 5 2.1 HDR method introduction - Exposure Fusion [2] 9 2.2 HDR quality assessment and evaluation 11 2.2.1 Image Enhancements 11 2.2.2 HDR Survey- Perceived Dynamic Range of HDR Images 17 2.2.3 Influence of HDR Reference on Observers Preference Evaluation 21 2.2.4 Two-Alternative Forced Choice- A Platform for Subjective Image Quality Evaluation on Mobile Devices 22 2.2.5 No-Reference Image Quality Assessment for High Dynamic Range Images 24 2.2.6 Benchmarking of objective quality metrics for HDR image quality assessment 24 Chapter 3. Proposed Method 26 3.1 Evaluation base on brightness 26 3.1.1 Choosing candidate pixel base on brightness using HLS color space 27 3.1.2 Mix Image 33 3.2 Evaluation base on foreground pixel 39 3.2.1 Foreground Pixel and Detail Variance 39 3.2.2 Saturation Measure by Exposure Fusion [2] 42 3.2.3 Foreground pixel based saturation measurement: S_fpg 45 3.2.4 Median Filter to Foreground pixel map 48 3.6 Subjective Survey to people’s preference to HDR images 51 3.6.1 Choose the BEST one 51 3.6.2 Two Alternative Forced Choice 54 Chapter 4. Experiment Results and Discussions 57 4.1 Producing HDR Image 57 Tool 57 Procedure 58 Photomatix pro 60 4.2.1 Pearson correlation coefficient 62 4.2.2 Spearman's rank correlation coefficient – SROCC 63 4.3.1 Results of Choice the best one 64 A. Natural image and colorful image 64 B. Daytime HDR and nighttime HDR 68 C. Subjective Survey Disscussion 71 D. Exception 85 E. The results of applying Median Filter to foreground pixel map 87 4.3.2 Results of 2AFC 88 F. Results and Disscussion 89 Chapter 5. Conclusion and Future Work 97 5.1 Conclusion 97 5.2 Future Work and Application 98 References 99 Appendix 101

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