摘要翻译：
图像质量评估的一个成功方法是比较畸变图像与其参考图像之间的结构信息。然而，提取对我们的视觉系统在感知上重要的结构信息是一项具有挑战性的任务。本文采用一种基于稀疏表示的方法来解决这个问题，并提出了一个新的度量指标&EMPH{基于稀疏表示的质量}(SPARQ)\EMPH{索引}。该方法将参考图像的固有结构学习为一组基向量，使得图像中的任何结构都可以用这些基向量中的少数几个的线性组合来表示。采用这种稀疏策略是因为已知它能产生与哺乳动物初级视皮层中存在的简单细胞的感受野定性相似的基向量。通过比较参考图像和畸变图像的结构，根据学习的类似皮层细胞的基向量来估计畸变图像的视觉质量。我们的方法在六个公开的主题图像质量评估数据集上进行了评估。提出的SPARQ指数与所有数据集的主观评分一致地显示出高度的相关性，并且性能更好或与最先进的状态相当。
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英文标题：
《Sparse Representation-based Image Quality Assessment》
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作者：
Tanaya Guha, Ehsan Nezhadarya, Rabab K Ward
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最新提交年份：
2013
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Multimedia        多媒体
分类描述：Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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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。
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英文摘要：
  A successful approach to image quality assessment involves comparing the structural information between a distorted and its reference image. However, extracting structural information that is perceptually important to our visual system is a challenging task. This paper addresses this issue by employing a sparse representation-based approach and proposes a new metric called the \emph{sparse representation-based quality} (SPARQ) \emph{index}. The proposed method learns the inherent structures of the reference image as a set of basis vectors, such that any structure in the image can be represented by a linear combination of only a few of those basis vectors. This sparse strategy is employed because it is known to generate basis vectors that are qualitatively similar to the receptive field of the simple cells present in the mammalian primary visual cortex. The visual quality of the distorted image is estimated by comparing the structures of the reference and the distorted images in terms of the learnt basis vectors resembling cortical cells. Our approach is evaluated on six publicly available subject-rated image quality assessment datasets. The proposed SPARQ index consistently exhibits high correlation with the subjective ratings on all datasets and performs better or at par with the state-of-the-art.
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PDF链接：
https://arxiv.org/pdf/1306.2727