A perceptual image compression framework is proposed in this work, including an adaptive picture-level just noticeable difference (PJND) prediction model and a perceptual coding scheme. Specifically speaking, a convolutional neural network (CNN) model is designed with the existing subjective image database to predict the PJND label for a given image. Then, the support vector regression model is utilized to determine the number of PJND levels. After that, a just noticeable difference generation algorithm is developed to compute the corresponding quality factor for each PJND level. Moreover, an effective perceptual coding scheme is devised for perceptual image compression. Finally, the accuracy of the proposed PJND prediction model and the performance of the proposed perceptual coding scheme are evaluated. The experimental results show that the proposed CNN based PJND prediction model achieves good prediction accuracy and the proposed perceptual coding scheme produces state-of-the-art rate distortion performances.