Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Hand geometry identification without feature extraction by general regression neural network
Expert Systems with Applications: An International Journal
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
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
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Image quality metrics have been widely used in imaging systems to maintain and improve the quality of images being processed and transmitted. Due to the close relationship between image quality and human visual perception, both computer scientists and psychologists have contributed to the development of image quality metrics. In this paper, a novel image quality metric using a colour appearance model is proposed. After the physical colour stimuli of the images being compared are transformed into perceptual colour appearance attributes, the distortion measures between the corresponding attributes are used to predict the subjective scores of image quality, by use of data-driven models: Multiple Linear Regression (MLR), General Regression Neural Network (GRNN) and Back-Propagation Neural Network (BPNN). Based on the data-driven model used, we have developed three image quality metrics, CAM_MLR, CAM_GRNN and CAM_BPNN. The experiments have shown that the performance of CAM_BPNN is better than the well-known image quality metric SSIM.