High dynamic range (HDR) images that include large differences in brightness levels are studied to address the lack of knowledge on the quality estimation method for real HDR images. For this, we earlier proposed a new metric, the independent signal-to-noise ratio (ISNR), using the independent pixel value as the signal instead of the peak value (PSNR). Next, we proposed the local peak signal-to-noise ratio (LPSNR), using the maximum value of neighboring pixels, as an improved version. However, these methods did not sufficiently consider human perception. To address this issue, here we proposed an objective estimation method that considers spatial frequency characteristics based on the actual brightness. In this method, the approximated function for human characteristics is calculated and used as a 2D filter on an FFT for spatial frequency weighting. In order to confirm the usefulness of this objective estimation method, we compared the results of the objective estimation with a subjective assessment. We used the organic EL display which has a perfect contrast ratio for the subjective assessment. The results of experiments showed that perceptual weighting improves the correlation between the SNR and MOS of the subjective assessment. It is recognized that the weighted LPSNR gives the best correlation.

Most people are concerned about their appearance, and the easiest way to change the appearance is to change the hairstyle. However, except for professional hairstylists, it is difficult to objectively judge which hairstyle suits them. Currently, oval faces are generally said to be the ideal facial shape in terms of suitability to various hairstyles. Meanwhile, field of visual perception (FVP), proposed recently in the field of cognitive science, has attracted attention as a model to represent the visual perception phenomenon. Moreover, a computation model for digital images has been proposed, and it is expected to be used in quantitative evaluation of sensibility and sensitivity called “kansei.” Quantitative evaluation of “goodness of patterns” and “strength of impressions” by evaluating distributions of the field has been reported. However, it is unknown whether the evaluation method can be generalized for use in various subjects, because it has been applied only to some research subjects, such as characters, text, and simple graphics. In this study, for the first time, we apply FVP to facial images with various hairstyles and verify whether it has the potential of evaluating impressions of female faces. Specifically, we verify whether the impressions of facial images that combine various facial shapes and female hairstyles can be represented using FVP. We prepare many combinational images of facial shapes and hairstyles and conduct a psychological experiment to evaluate their impressions. Moreover, we compute the FVP of each image and propose a novel evaluation method by analyzing the distributions. The conventional and proposed evaluation values correlated to the psychological evaluation values after normalization, and demonstrated the effectiveness of the FVP as an image feature quantity to evaluate faces.