Blind Image and Video Quality Assessment Using Natural Scene Statistics

项目摘要0310973 Alan Bovik德克萨斯大学@ Austin当前自动评估图像和视频数据质量的方法强调测量相对于参考的保真度。因此，假设“参考”图像/视频可用，并且质量损失被测量为与参考的偏差。然而，对于诸如视频点播、流式网络视频、到无线单元的视频服务和数字电视之类的实际应用，期望免除参考视频。因此，无参考（NR）质量评估（QA）非常重要。然而，NR QA的进展甚微，因为所使用的模型过于简单，主要限于涉及基于块的压缩视觉数据的应用程序。然而，成功的算法，正确预测的信号，已失真与其他类型的文物，如振铃和模糊造成的JPEG 2000图像压缩的质量，仍然是不存在的。我们正在研究一种新的方法，利用这一事实，即自然场景属于一个小的集合中的所有可能的图像/视频信号的空间。我们正在开发和调整描述自然场景的创新统计模型。我们已经表明，在图像/视频处理系统中的失真是不自然的，在这样的统计。因此，我们正在应用自然场景统计（NSS）模型的NR QA的视觉信号，假设来自自然场景的子空间。我们已经表明，NSS模型对于通过基于小波的方法压缩的静止图像的NR QA是有效的（例如，JPEG 2000）。我们正在开发新的NSS模型，用于基于小波的视频压缩和信道突发错误和快衰落信道中无线视频流的失真建模。

PROJECT ABSTRACT0310973Alan BovikUniversity of Texas @ AustinCurrent methods for automatically assessing the quality of image and video data emphasize measuring fidelity relative to a reference. Thus a "reference" image/video is assumed available, and loss of quality is measured as deviation from the reference. However, it is desirable to dispense with the reference video for practical applications, such as video-on-demand, streaming web video, video services to wireless units, and digital television. Thus No-Reference (NR) quality assessment (QA) is important. However, little progress has been made on NR QA since the models used have been simplistic and largely limited to applications involving block-based compressed visual data. However, successful algorithms for correctly predicting the quality of signals that have been distorted with other types of artifacts, such as ringing and blurring resulting from JPEG2000 image compression, remain nonexistent.We are working on a new approach that makes use of the fact that natural scenes belong to a small set in the space of all possible image/video signals. We are developing and adapting innovative statistical models that describe natural scenes. We have shown that distortions in image/video processing systems are unnatural in terms of such statistics. Thus we are applying Natural Scene Statistics (NSS) models for the NR QA of visual signals that are assumed to derive from the sub-space of natural scenes. We have already shown that NSS model are effective for NR QA of still images compressed by wavelet-based methods (e.g., JPEG2000). We are developing new NSS models for both wavelet-based video compression and for modeling distortions in wireless video streams from channel burst errors and fast-fading channels.