Image filter effectiveness characterization based on HVS

It is a quite common that acquired images are noisy and image filtering is a necessary step to enhance them. Usually image filtering effectiveness is characterized in terms of MSE or PSNR although nowadays it is well understood that these criteria do not always correspond adequately to visual perception of processed images. Recently several new measures of image quality have been proposed. In particular, two metrics, called PSNR-HVS and PSNR-HVS-M, were designed and successfully tested. Both take into account different sensitivity of a human eye to spatial frequencies, the latter one also accounts for the masking effects. Using these two metrics as well as a traditional PSNR and used by NASA metric DCTune, we have analyzed performance of five different filters (standard mean and median, sigma, Lee and DCT based filters) for a set of test images corrupted by an additive Gaussian noise with a wide set of variance values. It has been shown that there are many situations when PSNR after filtering improves while one or all other metrics manifest image quality decreasing. Most often this happens if noise variance is small and/or an image contains texture. Comparisons show that DCT based filter commonly outperforms other considered filters in the sense of denoised image visual quality. At the same time, the standard mean filter produces worse visual quality of processed images even its scanning window size is 3x3.