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We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.
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We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.
The addition of nitrogen doped mesoporous carbon spheres (NMCSs) to Portland cement-based materials was studied as an approach to produce materials of desired properties. Lower electrical resistivity and higher compressive strength were recorded in the presence of NMCSs, while the addition of a dispersion agent (Pluronic F127), caused an increase in electrical resistivity and strength reduction. This performance was related to the variation in zeta potential and hydrodynamic radius of NMCSs in alkaline medium. The zeta-potential of NMCSs was defined by the non-ionic surfactant F127. This led to a “poisoning” of the active NMCSs surface, consequently hindering the interaction of NMCSs with the cement-based material. Therefore, F127 is a non-suitable dispersing agent. In contrast, the presence of NMCSs alone enhances the desired cement-based materials properties, where reduced electrical resistivity and increased compressive strength were achieved, while cement hydration and pore network development were maintained close to NMCSs-free specimens.
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The addition of nitrogen doped mesoporous carbon spheres (NMCSs) to Portland cement-based materials was studied as an approach to produce materials of desired properties. Lower electrical resistivity and higher compressive strength were recorded in the presence of NMCSs, while the addition of a dispersion agent (Pluronic F127), caused an increase in electrical resistivity and strength reduction. This performance was related to the variation in zeta potential and hydrodynamic radius of NMCSs in alkaline medium. The zeta-potential of NMCSs was defined by the non-ionic surfactant F127. This led to a “poisoning” of the active NMCSs surface, consequently hindering the interaction of NMCSs with the cement-based material. Therefore, F127 is a non-suitable dispersing agent. In contrast, the presence of NMCSs alone enhances the desired cement-based materials properties, where reduced electrical resistivity and increased compressive strength were achieved, while cement hydration and pore network development were maintained close to NMCSs-free specimens.
