Investigation of Image Compression Based on Semantic Network and Deep Residual Variational Auto-Encoder

To achieve different compression rates for images while preserving important regions, a semantic network-based deep residual variational auto-encoder is introduced in this paper. The network is divided into two components, a semantic analysis network and an image compression network. The former evaluates the importance of image pixels and accurately locates important semantic regions and key information in the image. According to the semantic importance of different regions, the encoding strategy is dynamically adjusted. The latter utilizes a deep residual variational autoencoder to efficiently encode and decode images, while combining Lagrange multipliers to adjust the model flexibly and the weights of the bitrate. With this model, multiple compression rates have been implemented. Quality of reconstruction achieves better performance with compression at different rates. Finally, a semantic loss function is proposed to replace traditional compression loss functions. Extensive experiments conducts on several datasets, including the Kodak, CLIC and Tecnick TESTIMAGES, which were public datasets. Results demonstrate that our method can effectively improve the quality of image reconstruction at various compression rates. Compared with traditional methods, the peak signal-to-noise ratio is increased by an average of 2dB at the same bitrate, and the structural similarity index is the most close to 0.998. The subjective visual quality is better, especially when processing complex scene images, which can better preserve the details and textures of key objects. This approach can effectively avoid common distortions such as block effects and blurring in traditional methods.