生成模型压缩图片

https://arxiv.org/abs/1703.01467

传统的图像和视频压缩算法要依赖手动调整的编码器／解码器对（多媒体数字信号编解码器，codec)，缺乏适应性，对被压缩的数据也不可知。在这篇论文中，我们描述了生成式压缩的概念，也就是数据的压缩使用生成式模型。我们也表明这是一个值得追随的方向，可在图像和视频数据上取得更准确的、视觉上更享受的高压缩重建。我们也证明，相比于传统的变长度编码方案，生成式压缩在比特误码率上有更大的复原力（例如，从有噪声的无线通信频道）。 转自机器之心

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length coding schemes.