The trade between detector and optics performance is often conveyed through the Q metric, which is defined as the ratio of detector sampling frequency and optical cutoff frequency. Historically sensors have operated at Q~1, which introduces aliasing but increases the system modulation transfer function (MTF) and signal-to-noise ratio (SNR). Though mathematically suboptimal, such designs have been operationally ideal when considering system parameters such as pointing stability and detector performance. Substantial advances in read noise and quantum efficiency of modern detectors may compensate for the negative aspects associated with balancing detector/optics performance, presenting an opportunity to revisit the potential for implementing Nyquist-sampled (Q~2) sensors. A digital image chain simulation is developed and validated against a laboratory testbed using objective and subjective assessments. Objective assessments are accomplished by comparison of the modeled MTF and measurements from slant-edge photographs. Subjective assessments are carried out by performing a psychophysical study where subjects are asked to rate simulation and testbed imagery against a Delta-NIIRS scale with the aid of a marker set. Using the validated model, additional test cases are simulated to study the effects of increased detector sampling on image quality with operational considerations. First, a factorial experiment using Q-sampling, pointing stability, integration time, and detector performance is conducted to measure the main effects and interactions of each on the response variable, Delta-NIIRS. To assess the fidelity of current models, variants of the General Image Quality Equation (GIQE) are evaluated against subject-provided ratings and two modied GIQE versions are proposed. Finally, using the validated simulation and modified IQE, trades are conducted to ascertain the feasibility of implementing Q~2 designs in future systems
