Vision-Enhanced Formulation Of Signal-To-Noise Ratio For Imaging Systems

When evaluating an imaging system, it is important to have a confident evaluation measure as well as an understanding of the limitations of the evaluation measure. The signal-to-noise ratio (SNR) and several variants such as the peak signal-to-noise ratio (PSNR) have been used abundantly as quality measures in imaging and video systems. A debate as to whether or not SNR accurately reflects human perception in some cases has attempted to dissuade the use of SNR but SNR is still used in basic research as a quality measure. Recent work for evaluating video sequences suggests that SNR can follow the human perception trend if the proper formulation is used. This paper suggests that SNR can be a valid measure and follow human perception for evaluating quality if a proper formulation of SNR is constructed. The proper formulation must be based on recognition of vision system attributes. In particular, this paper suggests a new variant of the basic PSNR measure for evaluating single frame images based on recognition of the vision spatial attributes. In addition, this paper suggests a new integrated and motion-compensated variant of the PSNR which evaluates video sequences based on vision attributes of temporal integration, motion blur and motion sharpening. The new variants of PSNR are introduced and demonstrated by an example along with justification based on actual measurements of human visual response.