本文選題：立體圖像 + 質(zhì)量評(píng)價(jià)��； 參考：《計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào)》2017年05期

【摘要】：立體圖像質(zhì)量評(píng)價(jià)是評(píng)價(jià)立體視頻系統(tǒng)性能的有效途徑,而模擬人類大腦神經(jīng)網(wǎng)絡(luò)進(jìn)行特征提取是立體圖像質(zhì)量評(píng)價(jià)的關(guān)鍵.為此,提出一種基于極限學(xué)習(xí)機(jī)的全參考立體圖像質(zhì)量評(píng)價(jià)方法,包括3個(gè)階段:1)對(duì)原始和失真立體圖像分別進(jìn)行特征描述,以左圖像,右圖像和獨(dú)眼圖作為輸入信息,采用包含3個(gè)隱層的極限學(xué)習(xí)機(jī)將圖像信息映射到特征空間,從而得到原始和失真立體圖像的特征描述;2)對(duì)原始和失真立體圖像的特征描述進(jìn)行相似性度量,從而得到原始和失真立體圖像的質(zhì)量特征;3)采用極限學(xué)習(xí)機(jī)建立得到的12維質(zhì)量特征與主觀評(píng)價(jià)值的回歸模型,并將訓(xùn)練得到的回歸模型用于測(cè)試階段,預(yù)測(cè)得到相應(yīng)的客觀質(zhì)量評(píng)價(jià)值.實(shí)驗(yàn)結(jié)果表明,文中方法在對(duì)稱和非對(duì)稱立體圖像數(shù)據(jù)庫(kù)都取得了較好的性能,與人類的主觀感知保持良好的一致性.
[Abstract]:Stereo image quality evaluation is an effective way to evaluate the performance of stereo video system, and feature extraction based on simulated human neural network is the key of stereo image quality evaluation. In this paper, a new method for evaluating the quality of full reference stereo images based on extreme learning machine is proposed, which includes three stages: 1) describing the original and distorted stereo images respectively, taking the left image, the right image and the one-eye image as the input information. The image information is mapped to the feature space by an extreme learning machine which contains three hidden layers, and the feature description of the original and distorted stereo images is obtained. (2) the similarity between the original and the distorted stereo images is measured. Thus the quality characteristics of the original and distorted stereo images are obtained. The regression model of 12 dimensional quality features and subjective evaluation values established by the extreme learning machine is used in the test stage. The corresponding objective quality evaluation value is obtained by prediction. The experimental results show that the proposed method achieves good performance in both symmetric and asymmetric stereo image databases and is in good agreement with human subjective perception.
【作者單位】： 寧波大學(xué)信息科學(xué)與工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61271021)
【分類號(hào)】：TP391.41

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