本文選題：客觀圖像質(zhì)量評價 + 稀疏處理�。� 參考：《浙江大學》2016年碩士論文

【摘要】：在數(shù)字圖像的獲取、壓縮、存儲和傳輸過程中,由于存在獲取設(shè)備的缺陷、壓縮編碼、存儲錯誤和傳輸錯誤等問題,使得圖片的質(zhì)量下降,增加了人對圖像內(nèi)容識別的障礙。另一方面,在數(shù)字圖像處理等領(lǐng)域中,圖像質(zhì)量的好壞直接代表了算法的性能優(yōu)劣。而且圖像質(zhì)量作為一種重要的評價指標,可以用作優(yōu)化圖像處理系統(tǒng)參數(shù)的重要反饋。但是由于人對于數(shù)字圖像的主觀質(zhì)量評價實驗復(fù)雜,受實驗環(huán)境影響大,且不可重復(fù)等各種因素,難以被大型應(yīng)用場景采納使用�？陀^圖像質(zhì)量評價作為一種預(yù)測數(shù)字圖像質(zhì)量,得出與人的主觀評價結(jié)果相一致的機器算法,開始被研究人員廣泛關(guān)注。稀疏處理是當前信號處理領(lǐng)域的研究熱點,其優(yōu)勢體現(xiàn)在對信號進行高效表示,利用少量非零變量表征原始的大量數(shù)據(jù),降低信號處理的復(fù)雜性。本文研究了稀疏處理的原理,并基于稀疏處理方法提出了自然圖像質(zhì)量評價方法,概括為:(1)調(diào)研了稀疏處理方法的基本原理,研究了核獨立分量分析算法的原理和相關(guān)應(yīng)用,利用核獨立分量分析對數(shù)據(jù)進行非線性映射使得線性不可分的信號非線性可分的思想,設(shè)計了基于核獨立分量分析的客觀圖像質(zhì)量評價方法。方法對提取出的特征進行了基于自然圖像統(tǒng)計學的統(tǒng)計分析,利用相關(guān)系數(shù)與圖像質(zhì)量產(chǎn)生映射關(guān)系。經(jīng)過實驗驗證,核獨立分量分析對圖像數(shù)據(jù)進行分解得到的分量,作為有效特征可以較為精確的預(yù)測圖像質(zhì)量。(2)由于分量的獨立性對于圖像質(zhì)量預(yù)測的精確性有直接影響,如何提取出更加獨立有效的特征成為了基于核獨立分量分析的圖像質(zhì)量評價方法的關(guān)鍵。使用頻域距離作為塊匹配方法的匹配要求,能夠?qū)D像采樣數(shù)據(jù)進行高效的篩選。之后,經(jīng)過主成分分析和核獨立分量分析分解圖像采樣數(shù)據(jù)得到特征。經(jīng)過實驗證實,利用此種方法獲得的特征經(jīng)過質(zhì)量綜合,能較為明顯的提升圖像質(zhì)量的預(yù)測精度。最后,論文總結(jié)了稀疏處理應(yīng)用于圖像質(zhì)量評價框架中的思想和關(guān)鍵,說明稀疏處理對于自然圖像質(zhì)量評價的研究意義,總結(jié)了論文中基于稀疏處理方法中的分量分析的圖像質(zhì)量評價方法,對未來工作進行了展望。
[Abstract]:In the process of obtaining, compressing, storing and transmitting digital images, the quality of images decreases due to the defects of acquisition equipment, compression coding, storage errors and transmission errors, which increases the obstacle of image content recognition. On the other hand, in the field of digital image processing, image quality directly represents the performance of the algorithm. As an important evaluation index, image quality can be used as an important feedback to optimize the parameters of image processing system. However, the subjective quality evaluation of digital images is difficult to be used in large scale applications because of the complexity of the experiments, the influence of the experimental environment, and the non-repeatability of the experiments. Objective image quality evaluation, as a kind of machine algorithm which can predict digital image quality, comes up with the result of subjective evaluation, and has been paid more and more attention by researchers. Sparse processing is a hot research topic in the field of signal processing. Its advantage lies in the efficient representation of the signal, the use of a small number of non-zero variables to represent the original large amount of data, and the reduction of the complexity of signal processing. In this paper, the principle of sparse processing is studied, and the evaluation method of natural image quality based on sparse processing is proposed. The basic principle of sparse processing is investigated, and the principle and application of kernel independent component analysis (ICA) algorithm are studied. An objective image quality evaluation method based on kernel independent component analysis (KICA) is designed by using the idea of nonlinear mapping of data to make the linear inseparable signal nonlinearity separable by kernel independent component analysis (KICA). Methods the extracted features were statistically analyzed based on natural image statistics, and the mapping relationship between correlation coefficient and image quality was generated. It is proved by experiments that the components obtained from the decomposition of image data by kernel independent component analysis (ICA) can accurately predict the image quality as an effective feature) because the independence of the components has a direct impact on the accuracy of image quality prediction. How to extract more independent and effective features has become the key of image quality evaluation based on kernel independent component analysis (KICA). Using frequency domain distance as the matching requirement of block matching method, image sampling data can be filtered efficiently. After that, the image sampling data are decomposed by principal component analysis (PCA) and kernel independent component analysis (ICA). It is proved by experiments that the features obtained by this method can obviously improve the prediction accuracy of image quality through quality synthesis. Finally, the paper summarizes the ideas and key points of sparse processing applied in image quality evaluation framework, and explains the significance of sparse processing in natural image quality evaluation. In this paper, the image quality evaluation method based on component analysis in sparse processing is summarized, and the future work is prospected.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前2條

1 劉書琴;毋立芳;宮玉;劉興勝;;圖像質(zhì)量評價綜述[J];中國科技論文在線;2011年07期

2 齊開悅;陳劍波;周異;;An Improved Scalar Costa Scheme Based on Watson Perceptual Model[J];Journal of Shanghai Jiaotong University;2008年01期

，

本文編號：1971771