# An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis
## 1 Introduction  
The rapid development of the Internet of Things (IoT) has greatly facilitated people's lives, and it has also led to an explosive increase in the amount of data transmitted by networks. The types of network services have developed from the original text and voice signals to image and video signals, which brings convenience for the transmission of information and also continuously improves the requirements for data transmission and storage. Therefore, in order to reduce the volume of images during transmission and storage to improve network transmission efficiency, obtaining a better recovery quality through a smaller compression size has long been the focus of research in the image field. Image compression technology can be generally divided into lossy compression and lossless compression. Lossless compression compresses images by removing statistical redundancy in the image [1]. The process is reversible and is usually used in scenes where image sharpness is high, such as medical images, such as performed in [2], scarce data images and so on. The lossy compression algorithm performs redundant processing on image information according to the principle that the human eye is insensitive to certain visual features. Compared with lossless compression technology, lossy compression is at the expense of removing invisible information from the human eye, in exchange for the promotion of the compression ratio. Common lossy compression algorithms can be divided into traditional methods based on mathematical statistics and neural network methods based on deep learning. Most of the traditional methods are considered from the statistical characteristics of the data and compress images by various mathematical algorithms; typically, these include predictive coding, as used in [3]; sub-band coding, as in [4]; JPEG, as in [5], and so on. Deep learning-based methods mostly use artificial neural networks to design image codecs. Benefitting from the strong learning ability ofthe neural network, these methods can study the characteristics of images through backpropagation and realize the compression of image information without too much prior knowledge. Although many methods of deep learning currently perform well in image compression, there are still several issues to be addressed. In general, the human eye has a different degree of attention to each area of the image. For example, in a portrait picture, the clarity and texture details of the characters in the foreground are more eye-catching than the background. With the current IoT multimedia data compression requirements, most images have an apparent distinction between the foreground and background. However, the existing compression method performs the same processing for each pixel of the picture, so in an image with low background importance, it does not make the best allocation for each compressed bit. Therefore, in this scenario, it is necessary to propose the corresponding compression technique for the optimal compression bit allocation problem with clear foreground-background discrimination images. At present, most of the pictures on the Internet will generate noise during the transmission process, which will have a negative impact on the compression and recovery of the images. Some excellent denoising methods have been proposed in [6], [7] and [8]. In the experiments in this paper, we used the Kodak dataset without noise to better verify the performance of the compression algorithm. The proposed image compression framework in this paper is shown in Figure 1, including the semantic analysis network and image compression network. We use the semantic analysis network to extract the essential semantic regions of the input image and calculate the compression level corresponding to each area and then use the image compression network to compress and decompress the image hierarchically.