On the Quantification of Visual Texture Complexity.

Complexity is one of the major attributes of the visual perception of texture. However, very little is known about how humans visually interpret texture complexity. A psychophysical experiment was conducted to visually quantify the seven texture attributes of a series of textile fabrics: complexity, color variation, randomness, strongness, regularity, repetitiveness, and homogeneity. It was found that the observers could discriminate between the textures with low and high complexity using some high-level visual cues such as randomness, color variation, strongness, etc. The results of principal component analysis (PCA) on the visual scores of the above attributes suggest that complexity and homogeneity could be essentially the underlying attributes of the same visual texture dimension, with complexity at the negative extreme and homogeneity at the positive extreme of this dimension. We chose to call this dimension visual texture complexity. Several texture measures including the first-order image statistics, co-occurrence matrix, local binary pattern, and Gabor features were computed for images of the textiles in sRGB, and four luminance-chrominance color spaces (i.e., HSV, YCbCr, Ohta's I1I2I3, and CIELAB). The relationships between the visually quantified texture complexity of the textiles and the corresponding texture measures of the images were investigated. Analyzing the relationships showed that simple standard deviation of the image luminance channel had a strong correlation with the corresponding visual ratings of texture complexity in all five color spaces. Standard deviation of the energy of the image after convolving with an appropriate Gabor filter and entropy of the co-occurrence matrix, both computed for the image luminance channel, also showed high correlations with the visual data. In this comparison, sRGB, YCbCr, and HSV always outperformed the I1I2I3 and CIELAB color spaces. The highest correlations between the visual data and the corresponding image texture features in the luminance-chrominance color spaces were always obtained for the luminance channel of the images, and one of the two chrominance channels always performed better than the other. This result indicates that the arrangement of the image texture elements that impacts the observer's perception of visual texture complexity cannot be represented properly by the chrominance channels. This must be carefully considered when choosing an image channel to quantify the visual texture complexity. Additionally, the good performance of the luminance channel in the five studied color spaces proves that variations in the luminance of the texture, or as one could call the luminance contrast, plays a crucial role in creating visual texture complexity.

Color-difference formula for evaluating color pairs with no separation: ΔENS.

All color-difference formulas are developed to evaluate color differences for pairs of stimuli with hairline separation. In printing applications, however, color differences are frequently judged between a pair of samples with no separation (NS) because they are printed adjacently on the same piece of paper. A new formula, ΔENS, has been developed for pairs of stimuli with NS. An experiment was conducted to investigate the effect of different color-difference magnitudes using sample pairs with NS. 1012 printed pairs with NS were prepared around 11 CIE recommended color centers. The pairs, representing four color-difference magnitudes of 1, 2, 4, and 8 CIELAB units were visually evaluated by a panel of 19 observers using the gray-scale method. Comparison of the present data based on pairs with NS, and previously generated data using pairs with hairline separation, showed a clear separation effect. A new color-difference equation for the NS viewing condition (ΔENS) is proposed by modifying the CIEDE2000 formula. The separation effect can be well described by the new formula. For a sample pair with NS, when the CIEDE2000 color difference is less than 9.1, a larger color difference leads to a larger lightness difference, but has no effect on the chromatic difference. When the CIEDE2000 color difference is greater than 9.1, the effect is the opposite. The new formula is recommended for future research to evaluate its performance in appropriate applications.

Gloss evaluation from soft and hard metrologies.

Recent advances in bidirectional reflectance distribution function (BRDF) acquisitions have provided a novel approach for appearance measurement and analysis. In particular, since gloss appearance is dependent on the directional reflective properties of surfaces, it is reasonable to leverage the BRDF for gloss evaluation. In this paper, we investigate gloss appearance from both soft metrology and hard metrology. A psychophysical experiment was conducted for the gloss assessment of 47 neutral-color samples. In the evaluation of gloss perception from gloss meter measurements, we report several ambiguous correspondences in the medium gloss range. In order to analyze and explain this phenomenon, the BRDF was acquired and examined using a commercial BRDF measuring device. With an improved correlation-to-visual perception, we propose a two-dimensional gloss model by combining a parameter, the standard deviation of the specular lobe, from Ward's BRDF model with measured gloss values.

Enhancing the dyeability of polypropylene fibers by melt blending with polyethylene terephthalate.

Attempts were made to modify polypropylene fibers by melt blending with polyethylene terephthalate in order to enhance the dyeability of the resultant fiber. Five blends of polypropylene/polyethylene terephthalate/compatibilizer were prepared and subsequently spun into fibers. Three disperse dyes were used to dye such modified fibers at boiling and 130 °C. The dyeing performance of the blend fibers, as well as the morphological, chemical, thermal, and mechanical properties, of the corresponding blends was characterized by means of spectrophotometry, polarized optical microscopy, scanning electron microscopy (SEM), FT-IR spectroscopy, differential scanning calorimetry (DSC), and tensile testing.