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Foveated model based on the action potential of ganglion cells to improve objective image quality metrics

In this work, a foveated model (FM) based on the action potential of ganglion cells in the human retina is employed to improve the results obtained by traditional and perceptual image quality metrics. LIVE and VAIQ image databases are used in the experiments to test and validate this model. Statistical techniques, such as the Pearson Linear Correlation Coefficient (PLCC), the Spearman Rank-Order Correlation Coefficient (SROCC) and the Root Mean Square Error (RMSE), are used to evaluate the performance of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) metrics, as well as their versions improved by the FM. The results are encouraging because the model proposed improve the performance of the metrics investigated.