【摘要】 基于图像的灰度值是由R、G、B三个颜色分量线性计算而得的事实，本文提出从R、G、B三个颜色分量提取特征并采用神经网络训练质量预测模型的基于自然度与结构失真的彩色图像盲质量评价算法。首先，通过金字塔分解和分裂归一化将图像R、G、B三个颜色分量转换到小波域，在小波域进行自然场景统计，提取衡量图像自然度失真的质量感知特征。其次，分别在R、G、B三个颜色分量梯度域提取衡量结构失真的特征；同时提取图像像素的矩与二维熵作为补充特征。最后，使用反向传播神经网络对图像的特征与主观分数进行模型训练得到质量预测模型。在Basic Study,Image Gamut,Local Contrast三个具有颜色失真的色域映射图像数据库上的实验表明，该方法在评价颜色失真方面具有优越的性能。更多还原

【Abstract】 Based on the principle that the grayscale value of an image is obtained through a linear calculation of the R, G, and B color components, this study proposes a blind quality assessment method for color images that accounts for both naturalness and structural distortions. The method extracts features from the three color channels and employs a neural network to train a quality prediction model. First, through pyramid decomposition and split normalization, the three color channels are transformed into the wavelet domain, where quality-aware features(QAFs) are extracted based on natural scene statistics to measure naturalness distortion. Second, QAFs are extracted from the gradient domain of the three color channels to measure structural distortion. Additionally, the moments and two-dimensional entropy of the three color channels are incorporated as supplementary features. Finally, a quality prediction model is constructed using a backpropagation neural network trained on the extracted features. Experimental results on three color gamutmapped image databases with color distortion, including Basic Study, Image Gamut, and Local Contrast, demonstrate that the proposed method achieves superior performance in assessing color distortion.更多还原