Digital images and human vision
Digital images and human vision
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
No-reference image quality assessment based on DCT domain statistics
Signal Processing
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
Fuzzy relational classifier trained by fuzzy clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visibility of wavelet quantization noise
IEEE Transactions on Image Processing
No-reference quality assessment using natural scene statistics: JPEG2000
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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Assessing quality of distorted/decompressed images without reference to the original image is a challenging task because extracted features are often inexact and there exist complex relation between features and visual quality of images. The paper aims at assessing quality of distorted/decompressed images without any reference to the original image by developing a fuzzy relational classifier. Here impreciseness in feature space of training dataset is tackled using fuzzy clustering method. As a next step, logical relation between the structure of data and the soft class labels is established using fuzzy mean opinion score (MOS) weight matrix. Quality of a new image is assessed in terms of degree of membership value of the input pattern corresponding to given classes applying fuzzy relational operator. Finally, a crisp decision is obtained after defuzzification of the membership value.