# An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis
## 2 Related Works  
Recently, benefitting from the generation of large-scale data sets, the development of robust models, and a large number of available computing resources, deep learning theory has made excellent progress in the field of image semantic analysis and image compression. The weakly supervised semantic classification network is booming with the rise of deep learning, allowing computers to  
Figure 1: Compression framework. understand pictures better. As the originator of the CNN network, LeNet [9] is composed of a convolution layer, pooling layer, full connection layer, and sigmoid as the activation function. For the first time, the convolution layer is used instead of a manual hand-craft feature, which allows the computer to understand the picture in its own way. The Imagenet Large-Scale Visual Recognition (ILSVRC) competition has stimulated the rapid development of semantic analysis networks. In 2012, AlexNet--proposed by [PERSON] and [PERSON] in [10]--exploited Rectified Linear Units (ReLu) as an activation function replacing sigmoid to solve gradient diffusion and adopted dropout to avoid overfitting, aiming to focus on the wider meaning of semantic feature representation. In the same year, Google Inception Net (GoogleNet) in [11] was proposed as the winner of ILSVRC. GoogleNet used a 22-layer deep network to avoid gradient disappearance, subtly adding two loss functions at different depths network to ensure the return of the gradient. In addition, a 1x1 convolution kernel was adopted to reduce the thickness of the feature map. Residual Network (ResNet), proposed by [PERSON] et al. in [12], achieved a colossal breakthrough: the bottleneck residual block and jump connection deepened the depth of the network without gradient disappearance and network degradation. The 152-layer-deep network made a great leap forward in improving the deeper semantic understanding of the image. Deep learning has also made excellent progress in the field of image compression. A super-resolution convolutional neural network (SRCNN) network for image compression was proposed in [13] by [PERSON] et.al., first applying deep learning methods in solving pixel-level image problems. It uses a three-layer convolution structure to sample a low-resolution image by doubling the cubic difference and reconstructs the image in the pixel domain through the network. Researchers at Google proposed recurrent neural networks based on convolution and deconvolution long short-term memory (LSTM) in [14]. Four compression models were designed without retraining when the compression ratio changed, which were a full connection or convolution/deconvolution residual encoder with and without LSTM, finally using the SSIM index as the evaluation standard. Further, they published a progressive method in 2016 [15], a full-resolution image compression method based on a neural network, which achieved a 4.3-8.8% higher area under the rate-distortion curve (AUC) compared with the existing compression methods, making it the first neural network image compression framework beyond JPEG. In 2017, [PERSON] et al. at Hong Kong Polytechnic University proposed an image compression method based on image content weighting in [16]. This method adds the concept of an importance map to the traditional self-encoder structure. The edge feature map extracted by the three-layer convolution neural network from the features map was output by the encoder as the importance map of the original image. However, this method focuses on the edge of objects instead of the whole region, and a certain compression ratio is maintained for each training of the network. In 2017, a CNN-based image compression framework was proposed [17] which included two CNN networks, compact convolutional neural network (ComCNN) and reconstruction convolutional neural network (RecCNN), which are used to encode and decode the original image, respectively.