Fundamentals of digital image processing
Fundamentals of digital image processing
Neural network design
No-reference image quality assessment based on DCT domain statistics
Signal Processing
No reference image quality assessment for JPEG2000 based on spatial features
Image Communication
No-reference noticeable blockiness estimation in images
Image Communication
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
IEEE Transactions on Image Processing
No-reference image quality assessment in contourlet domain
Neurocomputing
Kurtosis-based no-reference quality assessment of JPEG2000 images
Image Communication
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
No-reference image quality assessment using structural activity
Signal Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A No-Reference Metric for Perceived Ringing Artifacts in Images
IEEE Transactions on Circuits and Systems for Video Technology
Blind Image Quality Assessment Using a General Regression Neural Network
IEEE Transactions on Neural Networks
Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images
IEEE Transactions on Image Processing
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Images are compressed using lossy compression for fast transmission and efficient storage. Due compression artefacts quality of images are degraded. In web application, unavailability of an original image is a major challenge to evaluate quality of images. Therefore there is an immense need to develop a quality metric that will automatically assess quality without referring the original image. In this paper, no reference image quality assessment scheme using the machine learning approach is proposed. The block-based features brightness, contrast, local amplitude, texture and other parameters of the degraded images are calculated along with first order and second order statistical features in frequency domain. These features are given as inputs to well-trained back propagation neural network whose output is a quality score. The mean opinion score is used as target. The result indicates that accuracy of quality assessment is better in comparison with traditional mathematical predictors.