论文标题
压缩：用于端到端压缩研究的Pytorch库和评估平台
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
论文作者
论文摘要
本文介绍了Compressai，该平台提供了自定义操作，层，模型和工具来研究，开发和评估端到端图像和视频压缩编解码器。特别是，Compressai包括预先训练的模型和评估工具，以将学习方法与传统编解码器进行比较。因此，从最先进的端到端压缩的最先进的模型已经在Pytorch中重新成熟，并从头开始训练。我们还使用PSNR和MS-SSIM指标与比特量率报告了客观的比较结果，使用Kodak Image Dataset作为测试集。尽管此框架当前实现了仍在静止压缩的模型，但它旨在很快扩展到视频压缩域。
This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs. In particular, CompressAI includes pre-trained models and evaluation tools to compare learned methods with traditional codecs. Multiple models from the state-of-the-art on learned end-to-end compression have thus been reimplemented in PyTorch and trained from scratch. We also report objective comparison results using PSNR and MS-SSIM metrics vs. bit-rate, using the Kodak image dataset as test set. Although this framework currently implements models for still-picture compression, it is intended to be soon extended to the video compression domain.
