Blind Image and Video Quality Assessment Using Natural Scene Statistics

目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，假设有一个“参考”图像/视频可用，并以偏离参考来衡量质量损失。然而，为了实际应用，如视频点播、流媒体网络视频、无线单元的视频服务和数字电视，希望省略参考视频。因此，无参考（NR）质量评估（QA）非常重要。然而，由于使用的模型过于简单，并且很大程度上局限于涉及基于块的压缩视觉数据的应用程序，因此在NR QA方面几乎没有取得进展。然而，正确预测被其他类型的伪影（如JPEG2000图像压缩产生的振铃和模糊）扭曲的信号质量的成功算法仍然不存在。我们正在研究一种新的方法，利用自然场景属于所有可能的图像/视频信号空间中的一个小集合这一事实。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，图像/视频处理系统中的失真在这种统计方面是不自然的。因此，我们将自然场景统计（NSS）模型应用于假设来自自然场景子空间的视觉信号的NR QA。我们已经证明，NSS模型对于基于小波的方法（如JPEG2000）压缩的静止图像的NR质量保证是有效的。我们正在为基于小波的视频压缩和基于信道突发错误和快速衰落信道的无线视频流中的失真建模开发新的NSS模型。

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.