Blind Image and Video Quality Assessment Using Natural Scene Statistics

项目摘要0310973Alan Bovik 德克萨斯大学奥斯汀分校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，对于实际应用，例如视频点播、流式网络视频、无线单元的视频服务以及数字电视，希望省去参考视频。因此，无参考 (NR) 质量评估 (QA) 很重要。然而，NR QA 方面进展甚微，因为所使用的模型过于简单化，并且很大程度上仅限于涉及基于块的压缩视觉数据的应用。然而，正确预测因其他类型的伪像（例如 JPEG2000 图像压缩产生的振铃和模糊）而失真的信号质量的成功算法仍然不存在。我们正在研究一种新方法，该方法利用自然场景属于所有可能图像/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，就此类统计数据而言，图像/视频处理系统中的失真是不自然的。因此，我们将自然场景统计 (NSS) 模型应用于假设源自自然场景子空间的视觉信号的 NR QA。我们已经证明，NSS 模型对于基于小波方法（例如 JPEG2000）压缩的静态图像的 NR QA 是有效的。我们正在开发新的 NSS 模型，用于基于小波的视频压缩以及对无线视频流中因信​​道突发错误和快速衰落信道造成的失真进行建模。

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.