Han Shuo, Mo Bo, Zhao Jie, Xu Junwei, Sun Shizun, Jin Bo
School of Aerospace Engineering, Beijing Institute of Technology, Beijing100081, China.
Chongqing Chang'an Wang Jiang Industry Group Co., Ltd., Chongqing 400023, China.
Sensors (Basel). 2024 Oct 10;24(20):6503. doi: 10.3390/s24206503.
Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.
日益庞大的图像数据受到信息传输和重建等条件的限制，在信息时代越来越难以满足速度和完整性的要求。为了解决海量图像数据在信息传输中面临的紧迫问题，本文提出了一种基于无监督学习的高质量图像压缩算法。其中，提出了一种内容加权自动编码器网络，以在较小比特率的基础上实现图像压缩编码，解决熵率优化问题。采用二进制量化器进行编码量化，并使用重要性映射来实现更好的比特分配，进一步控制和优化压缩率。设计了一种适用于生成对抗网络图像压缩框架的多尺度鉴别器，以解决生成的压缩图像容易模糊和失真的问题。最后，通过不同权重的训练，使各尺度的失真最小化，从而使图像压缩能够实现更高质量的压缩和重建效果。实验结果表明，该算法模型能够保留图像细节，大幅压缩图像存储空间。其优势在于能够快速高效地扩展和压缩大量图像，实现图像压缩的高效处理。
