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研究生:韋岱延
研究生(外文):Dai-Yan Wei
論文名稱:基於內容分析之多運算子畫面尺寸調整與品質衡量機制
論文名稱(外文):Content-Based Multi-Operator Retargeting and Its Quality Evaluation
指導教授:蘇柏齊
指導教授(外文):Po-Chyi Su
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:89
中文關鍵詞:多運算子畫面調整機制前景物偵測濃縮影像品質衡量SIFT Flow線段扭曲幾何扭曲迴歸分析
外文關鍵詞:Multi-OperatorsForeground DetectionRetargetQuality EvaluationSIFT FlowLine DistortionGeometric DistortionRegression Analysis
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本論文研究提出基於畫面內容之多運算子影像尺寸調整機制,希望在顯示畫面於不同輸出設備時仍能保持畫質,本研究亦提出適用於此應用的畫質衡量模型,合理評估原始影像與修改後影像的差異。首先我們改良多運算子畫面調整機制SCAN,它包含了圖縫裁減(Seam carving)、邊緣裁切(Cropping)、增加圖縫(Add seams)與畫面縮放(Normalization)。本研究主要改善邊緣裁切步驟,透過前景物偵測將影像分類,根據類別及畫面中的物體以不同的視覺顯著圖決定適當裁切位置。此外,我們加入人臉與建築物偵測,避免出現於畫面邊緣的人臉可能遭受不當裁切,並判斷建築物是否為畫面重要內容。實驗結果顯示所提出的改良式多運算子畫面調整機制在各式影像中能有效維持內容完整。在畫質衡量模型中,我們利用SIFT Flow比較原始影像及濃縮影像的內容差異,考量可能出現的幾何扭曲及線段扭曲,根據畫面顯著物及語意相關程度,以類神經網路迴歸分析找出平均意見分數(MOS)對每種屬性的依據,進而得到更貼近於人眼主觀感受的評估。實驗結果顯示,與其他評估方法相較,我們所提出的模型更貼近於MOS的結果。
This research proposes a content-based multi-operator image retargeting scheme, enabling the retargeted images to preserve its content after adaptation in various displays. Besides, a quality evaluation model is also proposed to compare original images and retargeted images. The proposed multi-operator retargeting scheme is termed “SCAN” as it contains Seam caving, Cropping, Adding seams and Normalization (scaling). This research mainly concentrates on improving the step of content-based cropping in SCAN. We classify images into two categories via foreground detection and adopt different types of visual saliency to determine appropriate cropping limits. The face detection is also introduced to protect face areas appearing at the edges of an image from being removed. A building detection mechanism is employed to determine whether a building in an image is significant or not. The experimental shows that the improved multi-operator retargeting scheme can effectively preserve the content and objects’ shape when dealing with various images. In the proposed quality evaluation model, we make use of SIFT Flow to compare the contents of original and retargeted images and identify possible geometric distortion and line distortion. We further consider salient objects and image semantics in the evaluation process. With these attributes, we utilize the neural network regression model to determine the weights of every feature in order to fit the Mean Opinion Score (MOS). The results show that the proposed model is closer to MOS than other evaluation methods.
論文摘要 i
Abstract ii
Content iii
List of Figures vi
List of Tables ix
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Contribution 5
1.3 Thesis Organization 6
Chapter 2. Related Work 7
2.1 Image Retargeting 7
2.1.1 Common Content-Based Retargeting Methods 7
2.1.2 SCAN 9
2.2 Performance Evaluating of Retargeting 13
2.2.1 Subjective Evaluation 13
2.2.2 Objective Evaluation 13
Chapter 3. Improved SCAN 16
3.1 Overview 16
3.2 Object Detection 17
3.2.1 Foreground Detection 18
3.2.2 Building Detection 19
3.2.3 Face Detection 19
3.3 Visual Saliency Map 20
3.3.1 Visual Saliency Feature 20
3.3.2 Deep Saliency 23
3.4 Foreground Extraction 25
3.5 Content-Based Image Retargeting Scheme 27
3.5.1 Improved Content-Based Cropping 27
3.5.2 Cropping Limits Refinement 30
Chapter 4. Quality Evaluation Scheme 33
4.1 Overview 33
4.2 Preprocessing 34
4.3 Line Distortion 35
4.4 Geometric Distortion 37
4.5 Distortion Analysis 38
4.6 Image Semantics Analysis 39
4.6.1 Saliency 39
4.6.2 Semantic Segmentation 40
4.7 Regression 42
Chapter 5. Experiment Results 44
5.1 Retargeting Mechanism 44
5.1.1 Image Classification 44
5.1.2 Results of Content-Based Cropping 46
5.1.3 Comparison with Ours and Other Retargeting Schemes 49
5.2 Quality Evaluation Model 60
5.2.1 Comparison with Subjective and Objective Scores 60
5.2.2 Scores of Other Retargeting Mechanism and SCAN 64
Chapter 6. Conclusion and Future Work 69
Reference 71
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