This paper proposes a new quality assessment method without reference images based on the convolutional neural network (CNN) and deep regression forest. First, this method performs a local contrast normalization on the original images. Second, it subsequently uses CNN to extract the discriminant features of the image quality. Finally, it utilizes the deep regression forest to predict the image quality. The method does not require any manual features, which simplifies the process of image preprocessing. In addition, fewer convolution layers are beneficial to reduce the training time of the network. The application of deep strategy to integrate the regression forests improves the prediction accuracy of a single forest. On the LIVE and TID2008 databases, the experimental results show that the proposed method can predict the image quality well and has a good generalization performance with high accuracy. The proposed method achieves a state-of-the-art performance, especially in JPEG2000, Gaussian blur and white noise distortions.