This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the network on the LIVE database and achieve a linear Pearson correlation superior to state-of-the-art NR IQA methods. We also apply the network to the image forensics task of decoder-sided quantization parameter estimation and also here achieve correlations of r = 0.989.