Video quality estimation is crucial for the efficient management of video delivery services. Particularly with the advances in screen technology and content delivery networks, getting an accurate estimation of the video quality as it is actually perceived by the user, is a key factor in delivering high quality-of-experience. Psychometric scaling provides the tools to measure the impact that different types of impairments have on the delivered quality. In contrast to the more conventional subjective rating procedures, psychometric scaling does not suffer from biases and has significantly lower variability. However, the existing psychometric methods such as Maximum Likelihood Difference Scaling (MLDS) entail a large number subjective tests. Herein we present an adaptive approach that leads to improvement in the learning rate and, in turn, to resource-efficient video delivery systems.