We are interested in metrics for automatically predicting the compression settings for stereoscopic images so that we can minimize file size, but still maintain an acceptable level of image quality. Initially we investigate how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly coded stereoscopic image pairs. Our results suggest that symmetric, as opposed to asymmetric stereo image compression, will produce significantly better results. However, PSNR measures of image quality are widely criticized for correlating poorly with perceived visual quality. We therefore consider computational models of the Human Visual System (HVS) and describe the design and implementation of a new stereoscopic image quality metric. This, point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes at regions of high spatial frequency, using Michelson's Formula and Peli's Band Limited Contrast Algorithm. To establish a baseline for comparing our new metric with PSNR we ran a trial measuring stereoscopic image encoding quality with human subjects, using the Double Stimulus Continuous Quality Scale (DSCQS) from the ITU-R BT.500-11 recommendation. The results suggest that our new metric is a better predictor of human image quality preference than PSNR and could be used to predict a threshold compression level for stereoscopic image pairs.