Neuropsychological Guided Blind Image Quality Assessment via Noisy Label Optimization  

作　　者：Zhu Jinchi Ma Xiaoyu Liu Chang Yu Dingguo 

机构地区：[1]Communication University of Zhejiang,Hangzhou 310042,China

出　　处：《China Communications》2025年第2期173-187,共15页中国通信(英文版)

基　　金：supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200);the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).

摘　　要：Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.

关 键 词：blind image quality assessment deep neural network ELECTROENCEPHALOGRAM persistent homology 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]