We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-scale training data. For authentic distortions, we adopt a pre-trained CNN for image classification. The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction. We then fine-tune the entire model on target subject-rated databases using a variant of stochastic gradient descent. Extensive experiments demonstrate that the proposed model achieves superior performance on both synthetic and authentic databases. Furthermore, we verify the generalizability of our method on the Waterloo Exploration Database using the group maximum differentiation competition.
翻译:我们提出一个处理合成和真实扭曲的盲人图像质量评估的深线双线模型(BIQA),我们的模型由两个进化神经网络(CNN)组成,每个网络都专门从事一种扭曲情景。关于合成扭曲,我们先对CNN进行训练,对图像扭曲的类型和级别进行分类,在那里我们享有大规模培训数据。关于真实扭曲,我们采用经过预先训练的CNN来进行图像分类。两个CNN的特征是双线组合成一个统一的代表,用于最终质量预测。然后,我们用随机梯度梯度下降的变种,对目标标定数据库的整个模型进行微调。广泛的实验表明,拟议的模型在合成和真实数据库上都取得了优异性。此外,我们还利用群体最大差异竞争,核查我们在水卢探索数据库上的方法的通用性。