Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.

中文翻译：

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使用类小波变换的端到端优化多功能图像压缩


基于深度网络的端到端优化图像压缩在过去几年中取得了令人瞩目的进展。以前的研究通常采用压缩自动编码器，编码器部分首先将图像转换为潜在特征，然后对特征进行量化，然后将其编码为比特。转换和量化都会导致信息损失，导致难以最佳地实现任意压缩比。我们提出 iWave++ 作为一种新的端到端优化图像压缩方案，其中 iWave（一种经过训练的类小波变换）将图像转换为系数，而不会丢失任何信息。然后，系数可选地被量化并编码为比特。与之前的方案不同，iWave++具有通用性：单一模型同时支持无损和有损压缩，还可以通过简单调整量化尺度来实现任意压缩比。 iWave++ 还具有精心设计的熵编码引擎来逐步编码系数，以及用于有损压缩的去量化模块。实验结果表明，与基于深度网络的方法相比，有损 iWave++ 实现了最先进的压缩效率；在 Kodak 数据集上，有损 iWave++ 比 BPG 节省了 17.34% 的位数；无损 iWave++ 实现了与 FLIF 相当或更好的性能。我们的代码和模型可在 https://github.com/mahaichuan/Versatile-Image-Compression 获取。