Traditional quality estimators evaluate an image's resemblance to a reference image. However, quality estimators are not well suited to the similar but somewhat different task of utility estimation, where an image is judged instead by how useful it would be in terms of extracting information about image content. While full-reference image utility metrics have been developed which outperform quality estimators for the utility-prediction task, assuming the existence of a high-quality reference image is not always realistic. The Oxford Visual Geometry Group's (VGG) deep convolutional neural network (CNN) [1], designed for object recognition, is modified and adapted to the task of utility estimation. This network achieves no-reference utility estimation performance near the full-reference state of the art, with a Pearson correlation of 0.946 with subjective utility scores of the CU-Nantes database and root mean square error of 12.3. Other noreference techniques adapted from the quality domain yield inferior performance. The CNN also generalizes better to distortion types outside of the training set, and is easily updated to include new types of distortion. Early stages of the network apply transformations similar to those of previously developed full-reference utility estimation algorithms.