Arnon Amir, M. Lindenbaum
Computer Vision and Image Understanding
Objective image and video quality measures play important roles in a variety of image and video processing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment approaches in the literature are error sensitivity-based methods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural distortion as an estimate of perceived visual distortion. A computationally efficient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group Phase I FR-TV test data set. © 2003 Elsevier B.V. All rights reserved.
Arnon Amir, M. Lindenbaum
Computer Vision and Image Understanding
Opher Etzion
DEBS 2007
Vivek Tyagi, Hima P. Karanam, et al.
ICPR 2012
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009