...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Signal processing >No-reference JPEG-image quality assessment using GAP-RBf
【24h】

No-reference JPEG-image quality assessment using GAP-RBf

机译：使用GAP-RBf的无参考JPEG图像质量评估

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we present a novel no-reference (NR) method to assess the quality of JPEG-coded images using a sequential learning algorithm for growing and pruning radial basis function (GAP-RBF) network. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the functional relationship is approximated using GAP-RBF network. The advantage of using sequential learning algorithm is its capability to learn new samples without affecting the past learning. Further, the sequential learning algorithm requires minimal memory and computational effort. Experimental results prove that the prediction of the trained GAP-RBF network does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.
机译：在本文中，我们提出了一种新颖的无参考（NR）方法，该方法使用顺序学习算法来评估和扩展径向基函数（GAP-RBF）网络，从而评估JPEG编码图像的质量。通过考虑关键的人类视觉敏感度因素（例如边缘幅度，边缘长度，背景活动和背景亮度）来提取预测感知图像质量的特征。图像质量估计包括计算HVS特征与主观测试分数之间的功能关系。在此，使用GAP-RBF网络来近似功能关系。使用顺序学习算法的优势在于它能够学习新样本而不会影响过去的学习。此外，顺序学习算法需要最少的内存和计算量。实验结果证明，训练有素的GAP-RBF网络的预测确实模拟了平均意见得分（MOS）。将所提出的度量的主观测试结果与JPEG无参考图像质量指数以及全参考结构相似图像质量指数进行比较，并且观察到两者均优于。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号