本文選題：立體圖像 + 客觀評價�。� 參考：《天津大學》2016年碩士論文

【摘要】：隨著立體成像技術的不斷發(fā)展,準確、有效地評價立體圖像質量已成為立體技術領域的研究熱點及難點之一。立體圖像質量的評價方法分為主觀評價和客觀評價兩種。主觀評價由合格被試依據自身主觀感受對測試圖像質量給出評分,這種方法能夠真實準確地反映圖像的質量,但它耗時耗力,且可操作性較差。因而,建立一套有效的立體圖像質量客觀評價模型已成為立體成像技術的重點研究課題之一。論文在對立體圖像質量評價的研究背景、發(fā)展現狀、發(fā)展趨勢及其他相關理論進行闡述的基礎上,考慮到目前人類視覺系統(tǒng)的相關研究仍存在較大的局限性,提出采用正交局部保留投影和極端學習機的方法建立立體圖像質量評價系統(tǒng)。鑒于立體圖像具有復雜度高、信息量大的特點,論文選取正交局部保留投影法對圖像進行有效地降維處理,該方法可以在對圖像降維的同時保留不同類別圖像間的結構,可以更有效地提取出立體圖像的特征。極端學習機網絡具有參數選擇簡單、泛化性好等特點,但是該網絡具有一定的隨機性。鑒于此,論文提出采用經過遺傳算法優(yōu)化的極端學習機作為分類器,使評價系統(tǒng)可以獲取更好的分類識別性能。本文選取了380幅經過不同失真處理、覆蓋不同評分等級的立體圖像,其中154幅為訓練樣本,226幅為測試樣本。實驗結果表明,選用正交局部保留投影法作為特征提取方法,使用ELM分類器在測試樣本中的客觀評分正確率可以達到93.36%,比選用主成分分析法所能達到的92.03%的準確率有更好的表現。使用遺傳算法對網絡參數進行優(yōu)化后,ELM網絡的分類正確率可以達到96.03%,使評價系統(tǒng)的準確率有了明顯的提高。此外,本文還對不同神經網絡分類器的質量評價性能進行了分析比較。
[Abstract]:With the development of stereo imaging technology, accurate and effective evaluation of stereo image quality has become one of the hot and difficult research fields. The evaluation methods of stereo image quality can be divided into subjective evaluation and objective evaluation. The subjective evaluation is evaluated by the qualified subjects according to their subjective feelings. This method can reflect the image quality truthfully and accurately, but it is time-consuming and energy consuming, and its operability is poor. Therefore, the establishment of an effective objective evaluation model of stereo image quality has become one of the key research topics of stereo imaging technology. Based on the research background, development status, development trend and other related theories of stereo image quality evaluation, this paper considers that there are still some limitations in the research of human visual system. A stereo image quality evaluation system based on orthogonal local preserving projection and extreme learning machine is proposed. In view of the high complexity and large amount of information of the stereo image, the orthogonal local preserving projection method is selected to reduce the dimension of the image effectively. This method can reduce the dimension of the image while preserving the structure of different kinds of images. The feature of stereo image can be extracted more effectively. Extreme learning machine network is characterized by simple parameter selection, good generalization and so on, but it has some randomness. In view of this, the paper proposes to use the extreme learning machine optimized by genetic algorithm as the classifier, so that the evaluation system can obtain better classification and recognition performance. In this paper, 380 stereo images with different distortion processing are selected, of which 154 are training samples and 226 are test samples. The experimental results show that the orthogonal locally reserved projection method is used as the feature extraction method. The objective scoring accuracy of ELM classifier in test samples can reach 93.36, which is better than the 92.03% accuracy of principal component analysis. After the optimization of network parameters by genetic algorithm, the classification accuracy of ELM network can reach 96.03, which improves the accuracy of the evaluation system obviously. In addition, the quality evaluation performance of different neural network classifiers is analyzed and compared.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41;TP183

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