One important application of computational saliency models is to aid objective image quality assessment. Given this, it is necessary to evaluate existing state-of-the-art visual attention models for their performance by comparing them with eye-tracking data associated with a quality assessment task. Existing comparative studies compare the saliency models with psychophysical data taken for a free viewing task and non-distorted images and hence do not accurately measure the usability of the saliency model to improve quality assessment pooling. This study evaluates 5 different visual attention models based on a database that includes human eyetracking data taken under a quality assessment task. The proposed study is helpful in understanding what features are useful in improving the performance of computational visual saliency models specific to quality assessment as well as in providing a generic performance assessment framework for comparing different competing models of visual saliency.