JPEG is still the most widely used image compression algorithm. Most image compression algorithms only consider uncompressed original image, while ignoring a large number of already existing JPEG images. Recently, JPEG recompression approaches have been proposed to further reduce the size of JPEG files. However, those methods only consider JPEG lossless recompression, which is just a special case of the rate-distortion theorem. In this paper, we propose a unified lossly and lossless JPEG recompression framework, which consists of learned quantization table and Markovian hierarchical variational autoencoders. Experiments show that our method can achieve arbitrarily low distortion when the bitrate is close to the upper bound, namely the bitrate of the lossless compression model. To the best of our knowledge, this is the first learned method that bridges the gap between lossy and lossless recompression of JPEG images.

翻译：JPEG仍然是最广泛使用的图像压缩算法。多数图像压缩算法仅考虑未压缩的原始图像，忽略了海量已存在的JPEG图像。近年来，虽然提出了针对JPEG文件的进一步压缩方法，但这些方法仅考虑无损重压缩，而这仅是率失真理论中的特例。本文提出了一种统一的有损与无损JPEG重压缩框架，该框架包含学习型量化表和马尔可夫层次化变分自编码器。实验表明，当比特率接近上限（即无损压缩模型的比特率）时，我们的方法可实现任意低的失真。据我们所知，这是首个弥合JPEG图像有损与无损重压缩之间差距的学习方法。