Local Feature Enhanced Adversarial Network for the Blind Image Quality Assessment
Xiaomei Shi, Min Zhang, Shouhai Xia, Ruxue Zhang, Jun Feng
Abstract
As a hot research topic in the field of computer vision, blind image quality assessment (BIQA) can provide high-quality images for end-users and promote the development of other fields of computer vision. Although the existing BIQA based on convolution neural networks has made significant progress in synthetic distortion evaluation, it still cannot be well extended to authentic distortion and algorithm-related distortion. Therefore, this paper proposes a BIQA adversarial network with local feature enhancement to deal with this challenge. First, the ResNeSt50 network with local feature enhancement is used to extract the features of images, which effectively combines the overall semantic information and the local features of the images. Then, the mapping of distorted images to their quality scores is learned by the adversarial network. Extensive experiments demonstrate that the proposed method performs best on three categories of distorted scenario databases (nine databases) compared with state-of-the-art BIQA methods.
BibTeX
@inproceedings{icassp2023_localfeatureenha,
title = {Local Feature Enhanced Adversarial Network for the Blind Image Quality Assessment},
author = {Xiaomei Shi and Min Zhang and Shouhai Xia and Ruxue Zhang and Jun Feng},
booktitle = {ICASSP 2023},
year = {2023}
}