计算机科学
卷积神经网络
人工智能
变压器
模式识别(心理学)
特征提取
深度学习
图像质量
机器学习
人工神经网络
数据挖掘
图像(数学)
量子力学
物理
电压
标识
DOI:10.1016/j.neucom.2023.126437
摘要
Most deep learning approaches for image quality assessment use regression from deep features extracted by CNN (Convolutional Neural Networks). However, non-local information is usually neglected in existing methods. Motivated by the recent success of transformers in modeling contextual information, we propose a hybrid framework that utilizes a vision transformer backbone to extract features and a CNN decoder for quality estimation. We propose a shared feature extraction scheme for both FR and NR settings. A two-branch structured attentive quality predictor is devised for quality prediction. Evaluation experiments on various IQA datasets, including LIVE, CSIQ and TID2013, LIVE-Challenge, KADID-10 K, and KONIQ-10 K, show that our proposed models achieve outstanding performance for both FR and NR settings.
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