The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Model Selection and Error Estimation
Machine Learning
The image importance approach to human vision based image quality characterization
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Fast computation of geometric moments using a symmetric kernel
Pattern Recognition
No reference image quality assessment for JPEG2000 based on spatial features
Image Communication
Perceptual quality assessment based on visual attention analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Off-line handwritten word recognition using multi-stream hidden Markov models
Pattern Recognition Letters
Parallel implementation of Artificial Neural Network training for speech recognition
Pattern Recognition Letters
Natural image utility assessment using image contours
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Objective image quality assessment based on support vector regression
IEEE Transactions on Neural Networks
Content-partitioned structural similarity index for image quality assessment
Image Communication
Image segmentation algorithms based on the machine learning of features
Pattern Recognition Letters
Image quality assessment by discrete orthogonal moments
Pattern Recognition
Inter-image outliers and their application to image classification
Pattern Recognition
Real-time lip reading system for isolated Korean word recognition
Pattern Recognition
Image Feature Extraction Using 2D Mel-Cepstrum
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Color image segmentation using pixel wise support vector machine classification
Pattern Recognition
Local MAP estimation for quality improvement of compressed color images
Pattern Recognition
Hessian optimal design for image retrieval
Pattern Recognition
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Non-intrusive speech quality assessment with support vector regression
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
The effects of a visual fidelity criterion of the encoding of images
IEEE Transactions on Information Theory
Gradient information-based image quality metric
IEEE Transactions on Consumer Electronics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An SVD-based grayscale image quality measure for local and global assessment
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
Rate Bounds on SSIM Index of Quantized Images
IEEE Transactions on Image Processing
A perceptually motivated three-component image model-Part I: description of the model
IEEE Transactions on Image Processing
Perceptual Rate-Distortion Optimization Using Structural Similarity Index as Quality Metric
IEEE Transactions on Circuits and Systems for Video Technology
Detection of fungal damaged popcorn using image property covariance features
Computers and Electronics in Agriculture
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Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.