Retina Model based Adaptive Neurological Denoising for Improved Image Quality Assessment
Speaker:Prof. Guangtao ZHAI
Institute of Image Communication and Information Processing
Shanghai Jiao Tong University
Date & Time:21 Jan 2014 (Tuesday) 15:00 - 16:00
Venue:J322 (University of Macau)
Organized by:Department of Computer and Information Science


It has long been realized that the research of image quality assessment (IQA) should take into account properties of the human visual system (HVS). Earlier attempts of HVS model based IQA methods took the reverse engineering type of approach to approximately emulate each known functional components of the HVS. Given the high complexity of the human visual brain, rather than explicitly modelling the HVS from the biological level, in this paper we develop an information theoretic model of retina, which is the very first neurological component of the HVS that acts as a bridge between the raw visual input and deeper processing mechanisms in high level vision. Since the information rate of visual signals is far beyond the processing capability of the visual brain, the design principle of the retina model is ``perception as data reduction''. The retina is believed to act as a neurological denoiser that trims down the input data load while retaining vital information for visual perception. The neurological denoiser can be used in a correctional prefiltering stage for current IQA methods. The result also provides information theoretical justification for the empirical findings in recent IQA research that proper data reduction by down-sampling/low-pass filtering improves the performance of IQA metrics. By concatenating the proposed neurological denoiser with existing IQA metrics as a preprocessing step (i.e. filtering both the original and the distorted images before calculating the full-reference quality metrics), performance of many IQA metrics, e.g. PSNR and SSIM can be effectively enhanced. We show in this paper that the neurological denoiser can enhance the accuracy of straightforward algorithms e.g. PSNR, to a level on par with state-of-the-art image quality metrics on several image quality databases.


Prof. Zhai received the B.E. and M.E. degrees from Shandong University, Shandong, China, in 2001 and 2004, respectively, and the Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 2009. He is currently a Research Professor at the Institute of Image Communication and Information Processing, Shanghai Jiao Tong University. From August 2006 to February 2007, he was a Student Intern with the Institute for Infocomm Research, Singapore. From March 2007 to January 2008, he was a Visiting Student with the School of Computer Engineering, Nanyang Technological University, Singapore. From October 2008 to April 2009, he was a Visiting Student with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, where he was a Post-Doctoral Fellow from January 2010 to March 2012. His current research interests include multimedia signal processing and perceptual signal processing. He received the prestigious National Excellent Doctoral Dissertation Award (百优博) in 2013.