Open AccessEnhanced image no‐reference quality assessment based on colour space distribution
Author(s) -
Liu Hao,
Li Ce,
Zhang Dong,
Zhou Yannan,
Du Shaoyi
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0856
Subject(s) - artificial intelligence , image quality , computer vision , similarity (geometry) , computer science , feature (linguistics) , image (mathematics) , pattern recognition (psychology) , quality (philosophy) , mathematics , philosophy , linguistics , epistemology
In this study, the authors investigate the problem of enhanced image no‐reference (NR) quality assessment. For resolving the problem of the enhanced images, it is difficult to obtain reference images, this study proposes an NR image quality assessment (IQA) model based on colour space distribution. Given an enhanced image, our method first uses a gist to select a clear target image in which the scene, colour and quality are similar to the hypothetical reference images. And then, the colour transfer is used between the input images and target images to construct the reference image. Next, the appropriate IQA method is used to assess enhanced image quality. The absolute colour difference and feature similarity (FSIM) are used to measure the colour and grey‐scale image quality, respectively. Extensive experiments demonstrate that the proposed method is good at evaluating enhanced image quality for X‐ray, dust, underwater and low‐light images. The experimental results are consistent with human subjective evaluation and achieve good assessment effects.