Image compression has been an important research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and its use in image compression has gradually been increasing. In this paper, we present an energy compaction-based image compression architecture using a convolutional autoencoder (CAE) to achieve high coding efficiency. Our main contributions include three aspects: 1) we propose a CAE architecture for image compression by decomposing it into several down(up)sampling operations; 2) for our CAE architecture, we offer a mathematical analysis on the energy compaction property and we are the first work to propose a normalized coding gain metric in neural networks, which can act as a measurement of compression capability; 3) based on the coding gain metric, we propose an energy compaction-based bit allocation method, which adds a regularizer to the loss function during the training stage to help the CAE maximize the coding gain and achieve high compression efficiency. The experimental results demonstrate our proposed method outperforms BPG (HEVC-intra), in terms of the MS-SSIM quality metric. Additionally, we achieve better performance in comparison with existing bit allocation methods, and provide higher coding efficiency compared with state-of-the-art learning compression methods at high bit rates.

中文翻译：

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使用卷积自动编码器的基于能量压缩的图像压缩

几十年来，图像压缩一直是一个重要的研究课题。最近，深度学习在许多计算机视觉任务中取得了巨大的成功，其在图像压缩中的应用也逐渐增多。在本文中，我们提出了一种基于能量压缩的图像压缩架构，使用卷积自编码器 (CAE) 来实现高编码效率。我们的主要贡献包括三个方面：1）我们通过将其分解为几个下（上）采样操作，提出了一种用于图像压缩的 CAE 架构；2）对于我们的 CAE 架构，我们提供了能量压缩特性的数学分析，我们是第一个在神经网络中提出归一化编码增益度量的工作，它可以作为压缩能力的度量；3) 基于编码增益度量，我们提出了一种基于能量压缩的比特分配方法，在训练阶段为损失函数添加正则化器，帮助CAE最大化编码增益并实现高压缩效率。实验结果表明，我们提出的方法在 MS-SSIM 质量指标方面优于 BPG（HEVC-intra）。此外，与现有的比特分配方法相比，我们实现了更好的性能，并在高比特率下提供了比最先进的学习压缩方法更高的编码效率。