Abstract: Image compression is a key technology in the fields of image processing and communications and has long been a research hotspot in academia. This study systematically reviews the basic concepts and principles of image compression, distinguishing between lossless and lossy compression and introducing various encoding techniques. With regard to traditional compression methods, techniques based on the discrete cosine transform, discrete wavelet transform, vector quantization, and fractal compression are comprehensively analyzed, with discussions on their respective advantages, disadvantages, and applicable scenarios. Although these methods have played significant roles in the field of image compression, their limitations have gradually become apparent with further technological developments.In the context of deep learning-based image compression, the study focuses on the application of convolutional neural networks , recurrent neural networks , generative adversarial networks, as well as the recently emerging Transformer and diffusion model approaches. These methods achieve more efficient compression and image reconstruction by automatically learning image features. In terms of performance evaluation, key metrics such as compression ratio, peak signal-to-noise ratio, and structural similarity index are analyzed, and the study discusses both the application prospects and the challenges faced by image compression technologies in various fields.Finally, the paper outlines future development directions and research trends in image compression technology, suggesting that with the integration of deep learning and emerging technologies, intelligent image compression will become a crucial development direction in the future.

Key words: image compression, vector quantization, fractal compression, deep learning

摘要： 图像压缩是图像处理与通信领域的一项关键技术，一直以来是学术界的研究热点。研究对图像压缩的基本概念和原理进行了系统梳理，区分了无损压缩与有损压缩，介绍了各类编码技术。在传统压缩方法方面，对基于离散余弦变换、离散小波变换、矢量量化和分形压缩的技术进行了全面分析，探讨了它们的优缺点及适用范围。这些方法虽在图像压缩领域发挥了重要作用，但随着技术发展，其局限性也逐渐显现。针对深度学习领域的图像压缩技术，重点研究了卷积神经网络、循环神经网络、生成对抗网络以及近年来兴起的Transformer和扩散模型等方法在图像压缩中的应用。这些方法通过自动学习图像特征，实现了更高效的压缩和图像重构。在性能评估方面，分析了压缩比、峰值信噪比和结构相似性指数等关键指标，并探讨了图像压缩技术在不同领域的应用前景和面临的挑战。最后，对未来图像压缩技术的发展方向和研究趋势进行了展望，指出随着深度学习与新兴技术的结合，智能图像压缩将成为未来的重要发展方向。

关键词: 图像压缩, 矢量量化, 分形压缩, 深度学习