Image quality measure is one of the important parts of the video and image engineering. The objective quality assessment methods had experienced three phases. The first phase was characterized as MSE (mean square error) and PSNR (peak signal noise ratio) and so on. Their main theory was based on the by-pixel difference accounting

whose result could be sensible to the difference between two images. But sometime it was too sensible to have well correlation with the subjective one. The second phase was characterized as the PQS[1] and JDM[5] and so on. Their main theory was based on the simulation of the human visual system. But its model always too complicated to realize. Now it was more popular to measure the image quality by detecting the characteristic feature of an image. The singular value could always be the most important information of an image. It had well stability to some extent quality distortion such as noise

scale variety

rotation

clipping and so on. So it was suitable for singular value of an image to be used as the feature parameter. In this paper we combined the visual masking effect and singular value decomposition together and designed a model for objective quality measure of an image. The original and the distorted image were first pre-processed according to visual masking effect and then their image matrixes were transformed into vectors by singular value decomposition. By comparing the angle between singular vectors of them

the quality of an image can be measured. It could improve the sensibility of the traditional SVD method by taking the human visual system properties into account and its result had closer correlation with the subjective one.