中圖分類號(hào)： TP391
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.08.010
引用格式： 劉錫澤，李志龍，何欣澤，等. 基于多任務(wù)學(xué)習(xí)的無參考超分辨圖像質(zhì)量評(píng)估[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(8)：60-64.

No reference super resolution image quality assessment based on multi-task learning

Abstract： Image super-resolution reconstruction is to recover the corresponding high resolution images from low resolution images, which is a classic problem in computer vision. In order to improve the inconsistency between traditional super-resolution image quality evaluation methods and visual perception, a super-resolution image quality evaluation network based on multi-task learning is proposed. The network adopts a multi-task learning method to learn the local frequency features and quality scores of the image respectively. The local frequency features are used to assist the network in the regression of the image quality scores to improve the accuracy and generalization ability of score prediction. In addition, adding coordinate attention blocks to the network to further enhance the predictive ability of the model. The experimental results show that the SROCC and PLCC of the proposed algorithm on the QADS dataset are better than the current advanced no-reference super-resolution image quality evaluation methods.

Key words : super-resolution image quality assessment；multi-task learning；local frequency features；coordinate