Deviation Based Pooling Strategies For Full Reference Image Quality Assessment
This work addresses the need for more robust pooling strategies in perceptual image quality assessment, though it is incremental as it builds on existing deviation-based methods.
The paper tackled the problem of pooling strategies for full reference image quality assessment (IQA) by proposing mean absolute deviation (MAD) pooling, which outperformed existing mean and standard deviation pooling methods across a wider range of IQA models, as shown in experimental results.
The state-of-the-art pooling strategies for perceptual image quality assessment (IQA) are based on the mean and the weighted mean. They are robust pooling strategies which usually provide a moderate to high performance for different IQAs. Recently, standard deviation (SD) pooling was also proposed. Although, this deviation pooling provides a very high performance for a few IQAs, its performance is lower than mean poolings for many other IQAs. In this paper, we propose to use the mean absolute deviation (MAD) and show that it is a more robust and accurate pooling strategy for a wider range of IQAs. In fact, MAD pooling has the advantages of both mean pooling and SD pooling. The joint computation and use of the MAD and SD pooling strategies is also considered in this paper. Experimental results provide useful information on the choice of the proper deviation pooling strategy for different IQA models.