两步预测编码图像质量是一种较好的方法

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要翻译：
全参考(FR)图像质量评估(IQA)模型假设高质量的“原始”图像作为衡量感知图像质量的参考。然而，在许多应用中，参考图像是高质量的假设可能是不真实的，导致不正确的感知质量预测。为了解决这一问题，我们提出了一种新的两步图像质量预测方法，该方法将无参考和全参考感知质量测量结合到质量预测过程中。无参考模块解释源（参考）图像可能不完美的质量，而全参考组件测量源图像与其可能进一步失真的版本之间的质量差异。一个简单但非常有效的乘法步骤将两个信息源融合成一个可靠的客观预测分数。我们在一个最近设计的主观图像数据库上评估了我们的两步方法，并与全参考方法相比取得了突出的性能，尤其是当参考图像质量较低时。提议的方法可在https://github.com/xianxuyu/2stepqa上公开查阅
---
英文标题：
《Predicting Encoded Picture Quality in Two Steps is a Better Way》
---
作者：
Xiangxu Yu, Christos G. Bampis, Praful Gupta and Alan C. Bovik
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--

---
英文摘要：
  Full-reference (FR) image quality assessment (IQA) models assume a high quality "pristine" image as a reference against which to measure perceptual image quality. In many applications, however, the assumption that the reference image is of high quality may be untrue, leading to incorrect perceptual quality predictions. To address this, we propose a new two-step image quality prediction approach which integrates both no-reference (NR) and full-reference perceptual quality measurements into the quality prediction process. The no-reference module accounts for the possibly imperfect quality of the source (reference) image, while the full-reference component measures the quality differences between the source image and its possibly further distorted version. A simple, yet very efficient, multiplication step fuses the two sources of information into a reliable objective prediction score. We evaluated our two-step approach on a recently designed subjective image database and achieved standout performance compared to full-reference approaches, especially when the reference images were of low quality. The proposed approach is made publicly available at https://github.com/xiangxuyu/2stepQA
---
PDF链接：
https://arxiv.org/pdf/1801.02016

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：Applications Architecture Segmentation Mathematical Measurements 信息源 感知 step 测量 reference