  The process of quantifying image quality consists of engineering the quality
features and pooling these features to obtain a value or a map. There has been
a significant research interest in designing the quality features but pooling
is usually overlooked compared to feature design. In this work, we compare the
state of the art quality and content-based spatial pooling strategies and show
that although features are the key in any image quality assessment, pooling
also matters. We also propose a quality-based spatial pooling strategy that is
based on linearly weighted percentile pooling (WPP). Pooling strategies are
analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE
and TID2013 image databases.
