Abstract
Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance and speed at medium and high bitrates, while research on ultra low bitrates is limited. In this work, we propose a ultra low bitrates enhanced invertible encoding network guided by traditional transformation theory, experiments show that our codec outperforms existing methods in both compression and reconstruction performance. Specifically, we introduce the Block Discrete Cosine Transformation to model the sparsity of features and employ traditional Haar transformation to improve the reconstruction performance of the model without increasing the bitstream cost.
Abstract (translated)
近年来，已经提出并实现了很多深图像压缩方法，获得了显著的性能。然而，这些方法都致力于在 medium 和 high 比特率中优化压缩性能和速度，而关于低比特率的超低比特率的研究有限。在这项工作中，我们提出了一种基于传统变换理论的超低比特率增强逆向编码网络，实验证明，我们的编码在压缩和重构性能上都优于现有方法。具体来说，我们引入了块离散余弦变换来建模特征的稀疏性，并采用传统的哈勃变换来提高模型的重构性能，而不会增加带宽成本。
URL
https://arxiv.org/abs/2402.15744
