Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
No reference image quality assessment for JPEG2000 based on spatial features
Image Communication
Photo and Video Quality Evaluation: Focusing on the Subject
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
No-reference image quality assessment using structural activity
Signal Processing
International Journal of Communication Networks and Distributed Systems
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No-reference measurement of perceptual image quality is a crucial and challenging issue in modern image processing applications. One of the major difficulties is that some inherent features of natural images and artifacts are possibly rather ambiguous. In this paper, we tackle this problem using statistical information on image gradient profiles and propose a novel quality metric for JPEG2000 images. The key part of the metric is a histogram representing the sharpness distribution of the gradient profiles, from which a blur metric that is insensitive to inherently blurred structures in the natural image is established. Then a ringing metric is built based on ringing visibilities of regions associated with the gradient profiles. Finally, a combination model optimized through plenty of experiments is developed to predict the perceived image quality. The proposed metric achieves performance competitive with the state-of-the-art no-reference metrics on public datasets and is robust to various image contents.