We aim to improve telepathology images for diagnoses using compression based on information about human visual system. Underlying goal is to demonstrate utility of a visual discrimination model (VDM) for predicting observer performance. 100 ROIs from breast biopsy virtual slides at 5 levels of compression (uncompressed, 8:1, 16:1, 32:1, 64:1, 128:1) were shown to 6 pathologists to determine benign vs malignant. There was a decrease in performance as a function of compression (F = 14.58, p< 0.0001). The visibility of compression artifacts in the test images was predicted using a VDM. JND metrics were computed for each image including mean, median, ≥90th percentiles, and maximum. For comparison PSNR and SSIM were also computed. Image distortion metrics were computed as a function of compression ratio and averaged across test images. All of the JND metrics were found to be highly correlated and differed primarily in magnitude. Both PSNR and SSIM decreased with bit rate, correctly reflecting a loss of image fidelity with increasing compression. The correlation of observer performance in the ROC experiment with image distortion metrics is shown in Figures 3 and 4. Observer performance (Az) was nearly constant up to a compression ratio of 32:1, then decreased significantly for 64:1 and 128:1 compression. The initial decline in Az occurred around a mean JND of 3, Minkowski JND of 4, and 99th percentile JND of 6.5. Virtual pathology may be compressible to relatively high levels before impacting diagnostic accuracy and the VDM accurately predicts human performance.