Many recently proposed perceptual image quality assessment algorithms are implemented in two stages. In the first stage, image quality is evaluated within local regions. This results in a quality/distortion map over the image space. In the second stage, a spatial pooling algorithm is employed that combines the quality/distortion map into a single quality score. While great effort has been devoted to developing algorithms for the first stage, little has been done to find the best strategies for the second stage (and simple spatial average is often used). In this work, we investigate three spatial pooling methods for the second stage: Minkowski pooling, local quality/distortion-weighted pooling, and information content-weighted pooling. Extensive experiments with the LIVE database show that all three methods may improve the prediction performance of perceptual image quality measures, but the third method demonstrates the best potential to be a general and robust method that leads to consistent improvement over a wide range of image distortion types.
Although habitability, defined as the general possibility of hosting life, is expected to occur under a broad range of conditions, the standard scenario to allow for habitable environments is often described through habitable zones (HZs). Previous work indicates that stellar binary systems typically possess Stype or P-type HZs, with the S-type HZs forming ringtype structures around the individual stars and P-type HZs forming similar structures around both stars, if considered a pair. However, depending on the stellar and orbital parameters of the system, typically, there are also regions within the systems outside of the HZs, referred to as dead zones (DZs). In this study, we will convey quantitative information on the width and location of DZs for various systems. The results will also depend on the definition of the stellar HZs as those are informed by the planetary climate models.
A crucial issue associated with a TCAM coprocessor with weights is that no more rules can be enforced if the weights are exhausted. In this paper, the problem is identified and a rule grouping technique is proposed to solve the problem. The technique allows virtually unlimited number of rules with arbitrary rule structures to be enforced. It requires no special hardware support and can be readily implemented in a fully programmable network processor and a weight-based TCAM coprocessor.
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