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Contrast plays an important role in human visual perception while it is usually unsatisfactory due to various factors in the acquisition process of images. With numerous approaches proposed to enhance contrast, less work has been dedicated to contrast changed image quality assessment (IQA). In this work, we propose a quality assessment model based on dual-path feature-difference, which uses a dual-channel...
In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not...
Secure face spoof detection systems demand the capability to identify whether a face is from a real client or a portrait from a spoofer. Spoofing induces distortion in the image and also degrades the image quality. Analysis of distortion and the quality assessment of an image to identify spoof attack is the main consideration here. The existing methods in image distortion analysis, extracts the features...
The past decades have witnessed a growing development of image quality assessment (IQA). However, there is still much room to improve the IQA performance, especially for the no reference IQA problem. In this paper, a convolutional neural network (CNN) based approach is designed to predict the distortion type of an image and assess its quality without original reference. With the proposed CNN approach,...
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