2021/06/02 by Jari Korhonen, Korhonen, Jari, Yicheng Su +3
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Video Quality Assessment #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.2106.00918
arxiv created 2021/06/02 · openalex publication_date 2021/06/02 · arxiv updated 2021/06/03 · openalex created_date 2021/06/22 · openalex updated_date 2026/07/28
Promising results for subjective image quality prediction have been achieved during the past few years by using convolutional neural networks (CNN). However, the use of CNNs for high resolution image quality assessment remains a challenge, since typical CNN architectures have been designed for small resolution input images. In this study, we propose an image quality model that attempts to mimic the attention mechanism of human visual system (HVS) by using a recurrent neural network (RNN) for spatial pooling of the features extracted from different spatial areas (patches) by a deep CNN-based feature extractor. The experimental study, conducted by using images with different resolutions from two recently published image quality datasets, indicates that the quality prediction accuracy of the proposed method is competitive against benchmark models representing the state-of-the-art, and the proposed method also performs consistently on different resolution versions of the same dataset.