# An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis
## 4 Experiments
### Visual Quality Evaluation  
To verify the performance of the proposed compression model, we used the Kodak dataset [27] as a test to compare the results with the JPEG, BPG, and [PERSON]'s methods. To fairly compare the original uncompressed images with the compressed images, it is necessary to select an evaluation indicator with a specific reference value. The fundamental purpose of the compression model proposed in this paper is to improve the subjective visual quality of human eyes. Therefore, the image will be verified using the MS-SSIM indicator, which is excellent at evaluating visual quality, applying MS-SSIM to each of the RGB channels independently to average the results. MS-SSIM gives a score of 0-1 for the contrasted picture; the closer the value to 1, the higher the similarity between the restored image and the original image. Figure 6 shows the original images and the results produced by the following compression methods. To ensure the compression ratio of each technique is as equal as possible, we maintain the bpp of the three compression methods at around 0.75; for an input RGB three-channel image, the compression ratio is close to \(3\times(8/0.75)=32\) times. In the MS-SSIM indicator of the whole picture, the column of [PERSON]'s method performed best, the method proposed in this paper performed less well, and the JPEG performed worst. At the same time, besides the structural similarity comparison of the whole picture, we also carried out subjective visual and objective numerical comparisons of the essential semantic areas in each image. As shown in Figure 6, the blue and green boxes are used to mark the characters and graphics on the plane, the text on the hat, the face, and the accessories on the body. All the grades of comparison in the MS-SSIM indicators show that our method gets the highest score. Moreover, in terms of visual quality, compared with the JPEG, with a visible blocking effect and artifacts through the contrast after magnification, and [PERSON]'s method, with a noticeable blur in the details, our approach not only performs well in terms of sharpness and texture details but also avoids the blocking effect and artifacts that are common to JPEG methods. The compression bit is unevenly distributed in the entire picture in our method; the higher the semantic importance, the more compressed bits are allocated in the region. In other words, compared with the average distribution compression methods, the visual recovery quality of our approach is higher than the average value in the essential semantic part and lower in the semantic inconsequential region. Therefore, the core of the compression method proposed in this paper improves the quality of essential areas by sacrificing the quality of restoration of insignificant regions to obtain better subjective visual quality. Generally speaking, the scale of the essential semantic region is usually smaller than the unimportant part of an image, which is the reason why the method in this paper is slightly inferior to [PERSON]'s method in the MS-SSIM index of the whole picture. Moreover, we compared the recovery quality of the three methods at different compression ratios. The results are shown in Figure 7a. In the case of a large compression ratio, both [PERSON]'s and the improved method in this paper can obtain better performance indicators than JPEG. However, because the method in this paper sacrifices the clarity of the background region to obtain the clarity of the essential semantic area, the MS-SSIM is slightly lower than for [PERSON]'s method. Besides, we propose an SSIM evaluation index SI-SSIM based on semantic importance to distinctly reveal the improvement of the proposed method. Based on SSIM calculation, the structural similarity in semantic degree is obtained by assigning each pixel a weight corresponding to its semantic importance.