Image Compression and Transmission Optimization Based on Deep Learning

This research aims to explore optimization methods for image compression and transmission based on deep learning. Firstly, we Learn the compact representation of an image by using convolutional autoencoders to achieve lossy compression. Furthermore, we investigated methods for training image compression models using deep neural networks (DNN), exploring how to achieve efficient image compression while maintaining image quality through deep learning techniques. We also focused on the application of Deep Reinforcement Learning (Deep RL) in transmission optimization strategies, aiming to reduce transmission time or bandwidth usage while maintaining image quality. In the experimental section, we will discuss the selection and characteristics of the dataset, experimental design and setup, performance analysis of deep learning models, and the effectiveness evaluation of optimization strategies. In conclusion, we will demonstrate the effectiveness and potential application value of the proposed methods through result visualization and comparison. This research provides new directions and approaches for further research and application in the field of image transmission, aiming to enhance the efficiency and quality of image transmission.