Analyzing the market sentiment and forecasting movements in asset prices is extremely important and has been attempted by researchers and market practitioners. Asset volatilities, regardless of historical ones or implied from the option prices, are crucial barometers of the market. And this research proposes a new approach that combines image processing and machine learning to capture the relationships between sentiment-related features and asset movement. The proposed research is based on the tick-level SPY options transactions, and the dataset contains around 1.5 million trading records. Specially, we obtained the gap between the call surface and the put surface as the second-level implied volatility surface (IVS). After adopting the traditional convolutional neural network (CNN) to compare the predictive effects among implied volatility (IV) call surface, IV put surface, and IV gap surface, the results indicate that the IV gap provides the most significant predictability. Besides, our project creatively proposes the interframe difference approach to optimized CNN and a recurrent CNN (RCNN) to fully utilize spatial and temporal features of the IV surface data. According to experiment results, the directional accuracy of prediction ranges from 67.20% to 72.05% for asset movement forecasting at the millisecond level.