论文标题
PILC：实用图像无损压缩，端到端的GPU导向神经框架
PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework
论文作者
论文摘要
基于生成模型的图像无损压缩算法在改善压缩比方面取得了巨大的成功。但是，即使使用最先进的AI加速芯片，它们中大多数的吞吐量也小于1 MB/s，从而阻止了它们通常需要100 MB/s的真实世界应用。在本文中，我们提出了PILC，这是一种端到端图像无损压缩框架，使用单个NVIDIA TESLA V100 GPU实现200 Mb/s的压缩和减压，比以前最有效的速度快10倍。为了获得此结果，我们首先开发了一个AI编解码器，该AI编解码器结合了自动回归模型和VQ-VAE，在轻量级设置中表现良好，然后我们设计了一个低复杂性熵编码器，与我们的编解码器配合良好。实验表明，在多个数据集中，我们的框架压缩比PNG高30％。我们认为，这是将AI压缩推向商业用途的重要步骤。
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.
