Digital images and human vision
Digital images and human vision
Image quality: a multidimensional problem
Digital images and human vision
The visible differences predictor: an algorithm for the assessment of image fidelity
Digital images and human vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Study of subjective and objective quality assessment of video
IEEE Transactions on Image Processing
Improved direct LDA and its application to DNA microarray gene expression data
Pattern Recognition Letters
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
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
Informative gene selection and tumor classification by null space LDA for microarray data
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Wave atoms based compression method for fingerprint images
Pattern Recognition
MLSIM: A Multi-Level Similarity index for image quality assessment
Image Communication
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Numerous Image Quality Measures (IQMs) have been proposed in the literature with different degrees of success. While some IQMs are more efficient for particular artifacts, they are inefficient for others. The researchers in this field agree that there is no universal IQM which can efficiently estimate image quality across all degradations. In this paper, we overcome this limitation by proposing a new approach based on a degradation classification scheme allowing the selection of the ''most appropriate'' IQM for each type of degradation. To achieve this, each degradation type is considered here as a particular class and the problem is then formulated as a pattern recognition task. The classification of different degradations is performed using simple Linear Discriminant Analysis (LDA). The proposed system is developed to cover a very large set of possible degradations commonly found in practical applications. The proposed method is evaluated in terms of recognition accuracy of degradation type and overall image quality assessment with excellent results compared to traditional approaches. An improvement of around 15% (in terms of correlation with subjective measures) is achieved across different databases.