No-Reference Quality Assessment for Screen Content Images Based on Hybrid Region Features Fusion
No-Reference Quality Assessment for Screen Content Images Based on Hybrid Region Features Fusion
复制标题
基于混合区域特征融合的屏幕内容图像无参考质量评估
DOI：
10.1109/tmm.2019.2894939
复制
发表时间：
2019-01
影响因子：
7.3
通讯作者：
Luo Jun
中科院分区：
文献类型：
--
作者：
Zheng Linru;Shen Liquan;Chen Jianan;An Ping;Luo Jun
屏幕内容图像（screen content images，SCIs）在移动的等设备上广泛应用于以图像和视频为中心的应用，因此SCIs的研究受到越来越多的关注。因此，研究一种有效的图像质量评价方法对于指导和优化各种图像处理方法，提高用户体验具有重要意义。在本文中，我们提出了一个无参考的客观评估模型，包括SCI分割和局部和全局感知特征表示的分析。由于人类视觉系统是高度敏感的尖锐边缘，通常会遇到在SCI，我们利用方差的局部标准差，这是一个噪声鲁棒性指标区分尖锐边缘补丁（SEPES）和非SEPES的SCI。对于SEPES，我们执行两种特征提取。首先，熵和对比度特征提取的灰度共生矩阵，这是高度敏感的微观结构的变化。其次，利用局部相位相干性来捕获锐度损失。然后，采用平均池化的方法融合所有SEP的特征，以表示局部特征。我们进一步结合联合收割机的局部功能与全球功能，使用BRISQUE方法作为混合区域（HR）为基础的功能。最后，使用支持向量回归来学习回归模块，以训练将基于HR的特征映射到主观质量分数的映射函数。在屏幕图像质量评估数据库上的实验结果表明，该方法在预测屏幕图像质量方面的性能优于现有方法。
Research on screen content images (SCIs) attracts more attention as they are highly applied to image- and video-centric applications on mobile and other devices. It is important to develop an efficient image-quality assessment (IQA) method for SCIs because IQA can guide and optimize various image-processing methods for SCIs and improve user experience. In this paper, we propose a no-reference objective assessment model for SCIs including SCIs segmentation and the analysis of local and global perceptual feature representations. Since the human visual system is highly sensitive to sharp edges that are commonly encountered in SCIs, we utilize the variance of local standard deviation, which is a noise robust index to distinguish the sharp edge patches (SEPes) and non-SEPes of SCIs. For SEPes, we perform two kinds of feature extractions. First, the entropy and contrast features are extracted with a gray-level co-occurrence matrix, which are highly perceptive of microstructural change. Second, the local phase coherence is utilized to capture the loss in sharpness. Then, average pooling is adopted to fuse features obtained from all of the SEPes to represent the local features. We further combine local features with global features that are derived using the BRISQUE method as the hybrid region (HR)-based features. Finally, a regression module is learned using support vector regression to train the mapping function that maps HR-based features to subjective quality scores. Experimental results on the screen image-quality assessment database show that the proposed method can achieve better performance in visual-quality prediction for SCIs than the performance achieved by state-of-the-art methods.
登录
查看更多内容
影响因子：
10.6
作者：
Wang Shiqi;Ma Lin;Fang Yuming;Lin Weisi;Ma Siwei;Gao Wen
通讯作者：
Gao Wen
屏幕内容图像的减少参考质量评估
DOI：
10.1109/tcsvt.2016.2602764
发表时间：
2018
影响因子：
8.4
作者：
Wang Shiqi;Gu Ke;Zhang Xinfeng;Lin Weisi;Ma Siwei;Gao Wen
通讯作者：
Gao Wen
影响因子：
10.6
作者：
Sheikh, HR;Bovik, AC
通讯作者：
Bovik, AC
适用于全色度屏幕内容的混合色度采样率高效视频编码
DOI：
10.1109/tcsvt.2012.2223871
发表时间：
2013
影响因子：
8.4
作者：
Zhang, Peijun;Wang, Shuhui;Zhou, Kailun;Chen, Xianyi
通讯作者：
Chen, Xianyi
影响因子：
8.2
作者：
BARALDI, A;PARMIGGIANI, F
通讯作者：
PARMIGGIANI, F