0310973 Alan Bovik University of Texas @ Austin
Current 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.
