  Visual attention is very an essential factor that affects how human perceives
visual signals. This report investigates how distortions in an image could
distract human's visual attention using Bayesian visual search models,
specifically, Maximum-a-posteriori (MAP)
\cite{findlay1982global}\cite{eckstein2001quantifying} and Entropy Limit
Minimization (ELM) \cite{najemnik2009simple}, which predict eye fixation
movements based on a Bayesian probabilistic framework. Experiments on modified
MAP and ELM models on JPEG-compressed images containing blocking or ringing
artifacts were conducted and we observed that compression artifacts can affect
visual attention. We hope this work sheds light on the interactions between
visual attention and perceptual quality.
