RESUMEN
The quality of videos is the primary concern of video service providers. Built upon deep neural networks, video quality assessment (VQA) has rapidly progressed. Although existing works have introduced the knowledge of the human visual system (HVS) into VQA, there are still some limitations that hinder the full exploitation of HVS, including incomplete modeling with few HVS characteristics and insufficient connection among these characteristics. In this article, we present a novel spatial-temporal VQA method termed HVS-5M, wherein we design five modules to simulate five characteristics of HVS and create a bioinspired connection among these modules in a cooperative manner. Specifically, on the side of the spatial domain, the visual saliency module first extracts a saliency map. Then, the content-dependency and the edge masking modules extract the content and edge features, respectively, which are both weighted by the saliency map to highlight those regions that human beings may be interested in. On the other side of the temporal domain, the motion perception module extracts the dynamic temporal features. Besides, the temporal hysteresis module simulates the memory mechanism of human beings and comprehensively evaluates the video quality according to the fusion features from the spatial and temporal domains. Extensive experiments show that our HVS-5M outperforms the state-of-the-art VQA methods. Ablation studies are further conducted to verify the effectiveness of each module toward the proposed method. The source code is available at https://github.com/GZHU-DVL/HVS-5M.
RESUMEN
Growth regulating-factors (GRFs) are plant-specific transcription factors that help regulate plant growth and development. Genome-wide identification and evolutionary analyses of GRF gene families have been performed in Arabidopsis thaliana, Zea mays, Oryza sativa, and Brassica rapa, but a comprehensive analysis of the GRF gene family in oilseed rape (Brassica napus) has not yet been reported. In the current study, we identified 35 members of the BnGRF family in B. napus. We analyzed the chromosomal distribution, phylogenetic relationships (Bayesian Inference and Neighbor Joining method), gene structures, and motifs of the BnGRF family members, as well as the cis-acting regulatory elements in their promoters. We also analyzed the expression patterns of 15 randomly selected BnGRF genes in various tissues and in plant varieties with different harvest indices and gibberellic acid (GA) responses. The expression levels of BnGRFs under GA treatment suggested the presence of possible negative feedback regulation. The evolutionary patterns and expression profiles of BnGRFs uncovered in this study increase our understanding of the important roles played by these genes in oilseed rape.
