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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58171
Title: | 基於機器學習方法之影像品質鑑定模型 Machine Learning Based Image Quality Assessment Model |
Authors: | Li-Heng Chen 陳立恆 |
Advisor: | 貝蘇章 |
Keyword: | 人眼視覺系統,影像品質評估,結構相似性,支持向量機, Full reference image quality assessment (IQA),support vector regression (SVR),human visual system (HVS),retina,difference of Gaussian (DOG),color distortion,PSNR,SSIM,FSIM, |
Publication Year : | 2014 |
Degree: | 碩士 |
Abstract: | 如何準確的評估影像的品質在影像處理的研究及應用扮演著舉足輕重的腳色,傳統測量影像品質的方法是比較原始影像與失真影像的峰值信噪比(PSNR),但近年來許多研究發現PSNR所測量出來的影像失真度和人眼視覺系統(HVS)有所不符,換句話說,我們使用的PSNR所測量出來的影像品質好壞和人眼所感知的影像品質是不一致的,因此我們需要發展新的影像品質測量演算法來解決這個問題。
首先我們會以一些淺顯易懂的例子來說明PSNR不符合HVS的事實,接著會介紹一些其他人提出的方法,如SSIM, FSIM, MSVD等。最後提出我們利用機器學習的方法結合已存在的不同頻帶的影像品質特徵和色彩特徵,訓練出和人眼視覺系統有高度相關性的模型。此外,我們也試著以Saliency map 來改善現有的影像品質估測方法,對此我們也做了一系列的實驗。 The objective image quality assessment (IQA) plays a key role in the development of various multimedia applications. The release of new IQA dataset (TID2013) challenges the wildly used 2D IQA metrics (e.g. PSNR and SSIM) since they cannot handle the diversity of distortion types. In this thesis, we propose a machine learning approach IQA model with features extracted from different frequency band (DOG features). The color distortion is also considered in our system. The effectiveness of our IQA system is verified by comparing with subjective score on the available databases. The experimental results show the high consistency between MOS score and our metric. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58171 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 電信工程學研究所 |
Files in This Item:
File | Size | Format | |
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ntu-103-1.pdf Restricted Access | 9.21 MB | Adobe PDF |
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