Generative Compression

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

Deep Learning Methods for Efficient Image Coding

Video data makes up 58% of all internet traffic and is growing as self-driving car cameras, 4K televisions, and video surveillance systems continue to come online. Traditional heuristics based image and video codecs such as JPEG and HEVC have been successful thus far, however, these approaches lack the ability to leverage big data to gain massive insights. Six deep learning based approaches are proposed to tackle efficient image/video compression and image compression for machine classification