A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment
This work addresses a specific bottleneck in image quality assessment for researchers and practitioners, but it is incremental as it builds on existing methods rather than introducing a new paradigm.
The study tackled the overlooked problem of pooling strategies in image quality assessment by comparing state-of-the-art quality and content-based spatial pooling methods, showing that pooling matters alongside feature design, and proposed a linearly weighted percentile pooling strategy that achieved competitive results on standard databases like LIVE and TID2013.
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.