基于双目神经元响应的无参考立体图像质量评价

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Neuron Response

为了解决多失真图像的质量预测偏差问题,根据视觉生理心理学研究中人类大脑初级视皮层(V1)神经元细胞对视觉信息处理的过程,提出了一种无参考立体图像质量评价方法。首先对失真立体图像对进行Gabor滤波,构造了基于双目神经元响应的V1区模拟刺激模型;其次通过离散余弦变换(DCT)和去均值对比度归一化(MSCN),分别提取了失真立体图像对的DCT域和空间域的自然场景统计特征;最后采用支持向量回归(SVR)算法,建立了所提取特征和主观评价值间的映射关系,进而构建了预测立体图像质量的客观评价模型。基于公开数据库对所提模型进行了验证和对比。结果表明,所提方法可统一预测单失真和多失真立体图像的感知质量,比现有评价方法的性能更优。

In order to solve the problem of quality prediction deviation of multiply-distorted images, a method for no-reference stereoscopic image quality assessment is proposed according to the process of visual information processed by neurons in human primary visual cortex(V1) in the research of visual physiology and psychology. Firstly, Gabor filtering is performed on the distorted stereoscopic image pairs to construct a simulated stimulus model of the V1 layer based on the binocular neuron response. Second, with the discrete cosine transformation(DCT) and the mean subtracted contrast normalization(MSCN), the natural scene statistics features of those distorted stereoscopic image pairs in DCT domain and spatial domain are extracted, respectively. Finally, the support vector regression(SVR) is adopted to build the objective evaluation model for predicting stereoscopic image quality via establishing the mapping relationship between the extracted features and the subjective scores. The proposed model is verified and compared based on the public databases, and the results show that the proposed method can uniformly predict the perceptual quality of singly-distorted and multiply-distorted stereoscopic images with better performance than that of other existing evaluation methods.