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研究生:林敬迪
研究生(外文):Ching-Ti Lin
論文名稱:基於機器學習的盲蔽式立體圖像品質評估
論文名稱(外文):Blind Stereoscopic Image Quality Assessment Based on Machine Learning
指導教授:劉宗榮劉宗榮引用關係
指導教授(外文):Tsung-Jung Liu
口試委員:林嘉文劉冠顯
口試委員(外文):Chia-Wen LinKuan-Hsien Liu
口試日期:2018-01-12
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
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:2018
畢業學年度:106
語文別:中文
論文頁數:60
中文關鍵詞:圖像品質評估盲蔽式立體圖像機器學習
外文關鍵詞:image quality assessmentno referencestereoscopic imagesmachine learning
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我提出了一個可以自動評估立體圖像品質的盲蔽式3D圖像品質估測器(CAP-3DIQA),該模型架構分為兩階段,分別是分類層與預測層,首先會將失真圖像在分類層中進行分類,然後根據失真類型的總數將失真圖像分為若干個子集,用意是為了聚集有相同的失真類型特徵的圖像。經過分類層的處理後,將分類後的失真圖像集輸入預測層中,該層包含五種相異的預測通道,來分別預測圖像的品質分數。最後,我使用支持向量機(SVM)的回歸模型對最終的圖像品質評估進行預測,其中回歸模型是將五條通道的圖像品質預測的結果組合當作輸入。我所提出的架構分別在三種公開且常使用的資料庫上進行測試,這三種資料庫分別是LIVE 3D Image Quality Database Phase I、LIVE 3D Image Quality Database Phase II 和 MCL 3D Image Quality Database。在LIVE 3D Image Quality Database Phase I和 MCL 3D Image Quality Database中僅包含對稱性失真的立體圖像,而在LIVE 3D Image Quality Database Phase II 中則包含對稱性與非對稱性失真的立體圖像。實驗結果表明,我所提出的模型在立體圖像品質評估上有很顯著的效能,且與其他現有的估測器相比,也有很大的競爭力。
We proposed a blind image quality assessment model named classification and prediction for 3D images quality assessment (denoted by CAP-3DIQA) that can automatically evaluate the stereoscopic image quality. First, the model executed the classified process in the classification layer, then the process separated the distorted images into several subsets according to the total number of distortion types, in order to assemble the images with the same distortion type characteristics. After the process of classification layer, the classified distorted image set is fed into the predicted image quality layer that contains five different channels which predicted image quality score individually. Finally, we used the regression module of support vector machine (SVM) to evaluate the final image quality score which the input of the regression model is the combination of five channel’s output. The model we proposed is test on the three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II and MCL 3D Image Quality Database. LIVE Phase I and MCL databases contains only symmetric-distorted stereoscopic images, LIVE Phase II contains both symmetric-distorted and asymmetric-distorted stereoscopic images. The experimental results on these databases show that our proposed model leads to significant improved performance on quality prediction of stereoscopic images competition with other existing quality metrics.
摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 vi
第一章 緒論 1
1.1 前言 1
1.2 論文的動機與大綱 2
第二章 文獻探討與相關背景知識 4
2.1 文獻探討 4
2.2 相關背景知識 5
2.2.1 雙眼視覺合成圖 5
2.2.2 機器學習 6
第三章 論文研究方法 8
3.1 分類層 9
3.1.1 提取用於分類層的特徵 9
3.1.2 分類模型的概述 12
3.2 預測層 17
3.2.1 提取用於預測層的特徵 18
3.2.2 回歸模型的概述 26
第四章 實驗結果 41
4.1 資料庫介紹 41
4.2 實驗分析與結果討論 49
第五章 結論與未來展望 57
參考文獻 58
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