Image quality assessment based on distortion-aware decision fusion

Authors:
Peng Peng;Zenian Li
Affiliations:
School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada;School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

Citing 10
Cited 1

Combined full-reference image quality metric linearly correlated with subjective assessment

ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms

ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Perceptual visual quality metrics: A survey

Journal of Visual Communication and Image Representation
Image quality assessment based on a degradation model

IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity

IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics

IEEE Transactions on Image Processing
Image information and visual quality

IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images

IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

General-purpose image quality assessment based on distortion-aware decision fusion

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Generic image quality (IQ) metrics based on individual features are not capable of making accurate prediction across different distortion types. In this paper, we propose a two-stage scheme to overcome this limitation. At the first stage, the image distortion type is predicted by support-vector classifiers. At the second stage, decision-level fusion of three existing IQ metrics are conducted based on the k-nearest-neighbor (k-NN) regression where the acquired distortion-type knowledge is employed. When evaluated on the largest publicly-available IQ database which involves a large variety of distortion types, the proposed approach demonstrates impressive accuracy and robustness.