We propose new blur and blocking metrics and then present a no-reference image-quality assessment method using these blur and blocking metrics. To compute the blur metric, we first estimated a blur radius from a given image and its reblurred version by using edge differences and edge amplitudes. Because blurring in edge regions is generally more sensitive to human perception, the blur metric was estimated from the edge blocks. We also used kurtosis and structural similarity to better estimate the blur metric. To compute the blocking metric, the blocking artifact was modeled as a 2-D step function and the blockiness visibility was estimated by the brightness difference between adjacent blocks. After the blocky position was determined, the blocking metric was computed from the six differences between four adjacent blocks. Experimental results show that the objective quality scores correlated highly with the subjective quality scores.