Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
A survey of hybrid MC/DPCM/DCT video coding distortions
Signal Processing - Special issue on image and video quality metrics
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
A single-ended blockiness measure for JPEG-Coded images
Signal Processing
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A distortion measure for blocking artifacts in images based on human visual sensitivity
IEEE Transactions on Image Processing
A de-blocking algorithm and a blockiness metric for highly compressed images
IEEE Transactions on Circuits and Systems for Video Technology
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
Image quality assessment based on the contourlet transform
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
No-reference image quality assessment using structural activity
Signal Processing
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
No reference image quality assessment using fuzzy relational classifier
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Information Sciences: an International Journal
Entropy of gabor filtering for image quality assessment
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Expert Systems with Applications: An International Journal
Complex-Valued neuro-fuzzy inference system based classifier
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
International Journal of Communication Networks and Distributed Systems
Boosting weighted ELM for imbalanced learning
Neurocomputing
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In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.