English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

The objective image quality assessment (IQA) method was developed to replace subjective observer image quality evaluations in various applications. A reliable full reference color IQA method that allows reference and distorted images to be compared in a symmetric way is designed via three fusion steps described in this article. The three fusion steps include luminance channels fusion, similarity maps fusion, and features fusion. A fusion weight coefficient is designed to fuse the luminance channels of input images as an enhancement operator for features. The extracted SR (spectral residual), gradient, and chrominance features, by means of symmetric calculations for the reference and distorted images, are conducted via similarity fusion processing. Then, based on the human visual system (HVS) characteristics of achromatic and chromatic information receiving, a features fusion map represents the weighted sum of three similarity fusion maps. Finally, a deviation pooling strategy is utilized to export the quality score after features fusion. The novel method is called the features fusion similarity index (FFS). Various experiments are carried out based on statistical evaluation criteria to optimize the parameters of FFS, after which the proposed method of FFS is compared with other state-of-the-art IQA methods using large-scale benchmark single distortion databases. The results show that FFS performs with higher consistency with respect to subjective scores in terms of prediction accuracy, e.g., the PLCC can achieve at least 0.9116 accuracy and at most 0.9774 accuracy for four databases. In addition, the average running time of FFS is 0.0657 s—a value representing a higher computational efficiency.

Image Quality Assessment Based on Three Features Fusion in Three Fusion Steps