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 authors explore the influence of the structure of a texture image on the perception of its color composition through a series of psychophysical studies. They estimate the color composition of a texture by extracting its dominant colors and the associated percentages. They then synthesize new textures with the same color composition but different geometric structural patterns. They conduct empirical studies in the form of two-alternative forced choice tests to determine the influence of two structural factors, pattern scale and shape, on the perceived amount of target color. The results of their studies indicate that (a) participants are able to consistently assess differences in color composition for textures of similar shape and scale, and (b) the perception of color composition is nonveridical. Pattern scale and shape have a strong influence on perceived color composition: the larger the scale, the higher the perceived amount of the target color, and the more elongated the shape, the lower the perceived amount of the target color. The authors also present a simple model that is consistent with the results of their empirical studies by accounting for the reduced visibility of the pixels near the color boundaries. In addition to a better understanding of human perception of color composition, their findings will contribute to the development of color texture similarity metrics.