Posts Tagged ‘PSNR’

The curse of the big table

Wednesday, June 3rd, 2015

As an Area Editor for Pattern Recognition Letters, I’m frequently confronted with papers containing big tables of results. It is often the deblurring and denoising papers that (obviously using PSNR as a quality metric!) display lots of large tables comparing the proposed method with the state of the art on a set of images. I’m seriously tired of this. Now I’ve set my foot down, and asked an author to remove the table and provide a plot instead. In this post I will show what is wrong with the tables and propose a good alternative.


Evaluating noise filters

Saturday, September 1st, 2012

Most of the new papers that I come across that propose a new or improved way of filtering out noise from images use the Peak Signal-to-Noise Ratio (PSNR) as a means to evaluate their results. It has been shown again and again that this is not a good way of evaluating the performance of a filter. When people compute the PSNR for a filtered image, what they actually do is compare this filtered image to an undistorted one (i.e. known ground truth). This is very different from what the name PSNR implies: the ratio of peak signal power to noise power. Of course, that is something that cannot be measured: if we’d be able to separate the noise from the signal and measure the power of the two components, then we wouldn’t need to write so many papers about filters that remove noise! So instead, the typical PSNR measure in image analysis uses the difference between the filtered image and the original (supposedly noise-free) image, calls this difference the noise, and computes its power (in dB):