基于多核增强学习分类算法的立体图像舒适度评价 - 中国图象图形学报.docVIP

中图法分类号：TN919 文献标识码：A 文章编号： ： ( 张竹青，邵 枫蒋刚毅 宁波大学信息科学与工程学院，宁波 315211摘 要：（PSIP）PSIP训练集；其次，提取多个视差统计特征和神经学模型响应特征；然后，利用基于AdaBoost的多核学习算法来建立偏好标签与特征之间的关系模型，并分析偏好分类概率（即相对舒适度概率）与最终的视觉舒适度之间的映射关系。结果 在同一立体图像库上，与现有代表性回归算法相比较，本文算法的Pearson线性相关系数PLCC）在0.84以上，Spearman等级相关系数SRCC）在0.80以上，均优于其他模型的各评价指标；而在跨库测试中，本文算法的PLCC、SRCC指标均优于传统的支持向量回归算法。结论 相比于传统的回归算法，本文算法具有更好的评价性能，能够更为准确地预测立体图像视觉舒适度。 关键词： Objective visual comfort assessment model for stereoscopic images via multiple kernel boosting Zhang Zhuqing, Shao Feng, Jiang Gangyi (Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211 China) Abstract: Objective: To solve the problem in assessment algorithms of traditional three-dimensional visual comfort, which generally require a large amount of training data with subjective mean opinion scores to train a regression model, we propose a new visual comfort assessment model via multiple kernel boosting (MKL) method. Method: First, considering the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort, we select some representative stereoscopic images to generate preference stereoscopic image pairs (PSIPs) and construct a PSIP training set with a preference label set. Second, we extract multiple disparity statistics and feature type derived by estimating neural activity, associated with horizontal disparity processing. Then, a preference classification model is trained on the basis of the MKL method by taking the vector of the aforementioned differential features and corresponding preference label of each PSIP as input. Besides, a mapping strategy between classification probability and final predictive visual comfort is analyzed. Result: Experimental results demonstrate that the proposed method can obtain a Pearson linear correlation coefficient (PLCC) larger than 0.84 and Spearman’s rank correlation coefficient (SRCC) larger than 0.80, which are superior to those of other existing representative regre