A Content-adaptive Visibility Predictor for Perceptually Optimized Image Blending.

The visibility of an image semi-transparently overlaid on another image varies significantly depending on the content of the images. This makes it difficult to maintain the desired visibility level when the image content changes. To tackle this problem, we developed a perceptual model to predict the visibility of the blended results of arbitrarily combined images. Conventional visibility models cannot reflect the dependence of the suprathreshold visibility of the blended images on the appearance of the pre-blended image content. Therefore, we have proposed a visibility model with a content-adaptive feature aggregation mechanism, which integrates the visibility for each image feature (i.e., such as spatial frequency and colors) after applying weights that are adaptively determined according to the appearance of the input image. We conducted a large-scale psychophysical experiment to develop the visibility predictor model. Ablation studies revealed the importance of the adaptive weighting mechanism in accurately predicting the visibility of blended images. We have also proposed a technique for optimizing the image opacity such that users can set the visibility of the target image to an arbitrary level. Our evaluation revealed that the proposed perceptually optimized image blending was effective under practical conditions.