Blur images are often subjected to the loss of high frequency content during acquisition, compression and multimedia transmission. Hence, objective blur assessment is implemented to identify and quantify image quality degradation by blurriness artifact in order to maintain and control the quality of the images. In this paper, objective full-reference and no-reference blur assessments using edge information are presented with the aim to provide computational models that can automatically measure the amount of blurriness artifact such as Gaussian blur on the images. The amount of Gaussian blur on an image, also known as the final blur measurement is determined by averaging the sum of edge width over all detected edges which satisfy the edge criteria. The final blur measurement for all test images based on full-reference and no-reference implementations are also validated with subjective results. The validation results show that the objective full-reference and no-reference blur assessments correlate closely to perceptual image quality.