This paper describes an objective measurement method designed to assess the perceived quality for digital videos. The proposed approach can be used either in the context of a reduced reference quality assessment or in the more challenging situation where no reference is available. In that way, it can be deployed in a QoS monitoring strategy in order to control the end-user perceived quality. The originality of the approach relies on the very limited computation resources which are involved, such a system could be integrated quite easily in a real time application. It uses a convolutional neural network (CNN) that allows a continuous time scoring of the video. Experiments conducted on different MPEG-2 videos, with bit rates ranging from 2 to 6 Mbits/s, show the effectiveness of the proposed approach. More specifically, a linear correlation criterion, between objective and subjective scoring, ranging from 0.90 up to 0.95 has been obtained on a set of typical TV videos in the case of a reduced reference assessment. Without any reference to the original video, the correlation criteria remains quite satisfying since it still lies between 0.85 and 0.90, which is quite high with respect to the difficulty of the task, and equivalent and more in some cases than the traditional PSNR, which is a full reference measurement.