The investigation examines two methodologies by which to control the impairment level of digital video test materials. Such continuous fine-tuning of video impairments is required for psychophysical measurements of human visual sensitivity to picture impairments induced by MPEG-2 compression. Because the visual sensitivity data will be used to calibrate objective and subjective video quality models and scales, the stimuli must contain realistic representations of actual encoder-induced video impairments. That is, both the visual and objective spatio-temporal response to the stimuli must be similar to the response to impairments induced directly by an encoder. The first method builds a regression model of the Peak Signal-To-Noise Ratio (PSNR) of the output sequence as a function of the bit rate specification used to encode a given video clip. The experiments find that for any source sequence, a polynomial function can be defined by which to predict the encoder bit rate that will yield a sequence having any targeted PSNR level. In a second method, MPEG-2-processed sequences are linearly combined with their unprocessed video sources. Linear regression is used to relate PSNR to the weighting factors used in combining the source and processed sequences. Then the 'synthetically' adjusted impairments are compared to those created via an encoder. Visual comparison is made between corresponding I-, B-, and P-frames of the synthetically generated sequences and those processed by the codec. Also, PSNR comparisons are made between various combinations of source sequence, the MPEG-2 sequence used for mixing, the mixed sequence, and the codec-processed sequence. Both methods are found to support precision adjustment of impairment level adequate for visual threshold measurement. The authors caution that some realism may be lost when using the weighted summation method with highly compression-impaired video.