Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and scientific research. But despite rapidly growing research interests in learning-based image compression, no published method offers both lossless and near-lossless modes. In this paper, we propose a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression. In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals. We solve the joint lossy and residual compression problem in the approach of VAEs, and add autoregressive context modeling of the residuals to enhance lossless compression performance. In the near-lossless mode, we quantize the original residuals to satisfy a given $\ell_\infty$ error bound, and propose a scalable near-lossless compression scheme that works for variable $\ell_\infty$ bounds instead of training multiple networks. To expedite the DLPR coding, we increase the degree of algorithm parallelization by a novel design of coding context, and accelerate the entropy coding with adaptive residual interval. Experimental results demonstrate that the DLPR coding system achieves both the state-of-the-art lossless and near-lossless image compression performance with competitive coding speed.

翻译：无损和近无损图像压缩对医学、遥感、精密工程和科学研究等诸多技术领域的专业用户至关重要。尽管基于学习的图像压缩研究兴趣迅速增长，但尚无已发表方法同时支持无损和近无损模式。本文提出一种统一且强大的深度有损加残差（DLPR）编码框架，适用于无损和近无损图像压缩。在无损模式下，DLPR编码系统首先进行有损压缩，再对残差进行无损编码。我们采用变分自编码器（VAE）方法解决联合有损与残差压缩问题，并引入残差的自回归上下文建模以提升无损压缩性能。在近无损模式下，我们对原始残差进行量化以满足给定的$\ell_\infty$误差界，并提出一种可扩展的近无损压缩方案，该方案可针对可变$\ell_\infty$界限工作，而无需训练多个网络。为加速DLPR编码，我们通过新颖的编码上下文设计提高算法并行化程度，并利用自适应残差区间加速熵编码。实验结果表明，DLPR编码系统在具有竞争力的编码速度下，实现了最先进的无损和近无损图像压缩性能。