Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decompression with a single NVIDIA Tesla V100 GPU, 10 times faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use.
翻译:基于模型的无损图像生成压缩算法在改善压缩率方面取得了巨大成功。 但是,大多数压压率的通过量都低于1 MB/s, 即使是最先进的AI加速芯片也低于1 MB/s, 从而阻止了它们进入最真实世界的应用, 而这往往需要100 MB/s 。 在本文中, 我们建议 PILC, 一个端到端的无损图像压缩框架, 这个框架在压缩和降压方面都达到200 MB/s, 其压缩速度比之前的NVIDIA Tesla V100 GPU要快10倍。 为了获得这一结果, 我们首先开发了一个将自动递减模型和VQ- VAE结合起来的 AI 编码器, 在轻量环境下运行良好, 然后我们设计一个与我们的编码器运行良好的低复杂性 。 实验显示, 我们的框架在多个数据集中比 PNG 高出30% 的幅度。 我们认为这是将 AI 压缩推向商业用途迈出的重要一步 。