本文關(guān)鍵詞： 質(zhì)量評價 人腦 層狀皮質(zhì)模型 視覺感知　出處：《光電子·激光》2017年05期 　論文類型：期刊論文

【摘要】：通過模擬人腦視覺神經(jīng)接收視覺信息形成表面感知的處理機(jī)制,提出一種基于大腦層狀皮質(zhì)模型的全參考立體圖像的圖像質(zhì)量評價(IQA)方法。首先,分析大腦形成表面感知的過程,提出可運(yùn)用于立體圖像的IQA的層狀皮質(zhì)模型;然后依據(jù)模型得到各層的響應(yīng)輸出,構(gòu)建感知特征向量;最后利用機(jī)器學(xué)習(xí)算法,建立特征和質(zhì)量的關(guān)系模型,預(yù)測立體圖像質(zhì)量。實驗結(jié)果表明,本文方法在對稱立體圖像庫上的Pearson線性相關(guān)系數(shù)(PLCC)和Spearman等級系數(shù)(SROCC)高于0.91,在非對稱庫上高于0.93。與現(xiàn)有的相關(guān)方法相比,本文方法與主觀評價更加吻合,更適合立體圖像的評價和優(yōu)化。
[Abstract]:By simulating the processing mechanism of human visual nerve receiving visual information to form surface perception, an image quality evaluation method based on the layered cortex model of brain is proposed. Firstly, IQA is used to evaluate the image quality of all reference stereoscopic images. By analyzing the process of the formation of surface perception in the brain, a layered cortical model of IQA can be applied to stereoscopic images is proposed. Then, the response output of each layer is obtained according to the model, and the perception feature vector is constructed. Finally, the machine learning algorithm is used. The relationship model of feature and quality is established to predict the quality of stereo image. The experimental results show that, In this paper, the Pearson linear correlation coefficient and Spearman rank coefficient are higher than 0.91in symmetric stereo image database and 0.93in asymmetric database. Compared with the existing correlation methods, the method in this paper is more consistent with subjective evaluation. More suitable for stereo image evaluation and optimization.
【作者單位】： 寧波大學(xué)信息科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金(61271021)資助項目
【分類號】：R741.04;TP181
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本文編號：1517869