A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)

Authors:
Rony Ferzli;Lina J. Karam
Affiliations:
Department of Electrical Engineering, Arizona State University, Tempe, AZ;Department of Electrical Engineering, Arizona State University, Tempe, AZ
Venue:
IEEE Transactions on Image Processing
Year:
2009

Citing 4
Cited 19

Digital Video and HDTV Algorithms and Interfaces

Digital Video and HDTV Algorithms and Interfaces
Deringing cartoons by image analogies

ACM Transactions on Graphics (TOG)
Adaptive image coding with perceptual distortion control

IEEE Transactions on Image Processing
JPEG2000 encoding with perceptual distortion control

IEEE Transactions on Image Processing

No-reference perceptual image quality metric using gradient profiles for JPEG2000

Image Communication
No-reference image and video quality estimation: Applications and human-motivated design

Image Communication
Automatic parameter selection for denoising algorithms using a no-reference measure of image content

IEEE Transactions on Image Processing
Perceptual visual quality metrics: A survey

Journal of Visual Communication and Image Representation
Degradation based blind image quality evaluation

SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
No-reference quality metrics for eye fundus imaging

CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
No-reference video monitoring image blur metric based on local gradient structure similarity

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
NEOCR: a configurable dataset for natural image text recognition

CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Helping visually impaired users properly aim a camera

Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Correction, stitching and blur estimation of micro-graphs obtained at high speed

ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Causes and subjective evaluation of blurriness in video frames

Image Communication
SmartDCap: semi-automatic capture of higher quality document images from a smartphone

Proceedings of the 2013 international conference on Intelligent user interfaces
CONTENTUS--technologies for next generation multimedia libraries

Multimedia Tools and Applications
Efficient blur estimation using multi-scale quadrature filters

Signal Processing
A two-stage quality measure for mobile phone captured 2D barcode images

Pattern Recognition
No-reference blur image quality measure based on multiplicative multiresolution decomposition

Journal of Visual Communication and Image Representation
Iris classification based on its quality

ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
User-assisted reflection detection and feature point tracking

Proceedings of the 10th European Conference on Visual Media Production
No reference image quality assessment using blocked-based and frequency domain statistical features: a machine learning approach

International Journal of Communication Networks and Distributed Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.