Data compression of digitized images usually leads to pictures with two or more perceptually distinguishable coding artifacts. In this paper, percentage scaling is presented as a new method for analyzing the perceived quality of multiply impaired images. By this method, subjects assess, per coding level, the proportion each artifact contributes to the overall impairment of the perceived quality of the coded image. These proportions are expressed in percentages such that the total sum is always 100%. As an illustration, percentage scaling has been employed to evaluate the perceived quality of JPEG-coded images comprising three types of coding artifacts: blockiness, ringing and blur. In one experiment, eight subjects expressed in percentages how much they believed these artifacts contributed to the overall impairment. In addition, overall impairment as well as the perceived strengths of blockiness, ringing and blur was assessed on 11-point numerical category scales. The percentages were found to vary with the coding level. Blockiness appeared to be the most dominant artifact at high compression levels, whereas ringing turned out to be the most annoying artifact at low compression levels. The results support the claim that perceptually distinguishable coding artifacts can be represented by a set of orthogonal vectors in a multidimensional Euclidean space.