Real-Time Adaptive Image Compression

Deep learning will be a great way to do compression for sure, both for audio, video and images. I could see that one could download "knowledge sets" for these decompressors. Looking at Google Earth, download the supplemental "knowledge set" for overhead shots of cities and country side. Looking at people, download the supplemental "knowledge set" for faces and clothing, etc.

Basically each domain you want to do well in you need a knowledge set that is trained on that data. Then you need a discriminator on the compression side to classify an image or subregions of an image into those categories.

If you can make the knowledge sets downloadable on demand and then cached you can be incredibly efficient over the long term, while maintaining very small initial download sets. I think evolveable knowledge sets over time also ensure that the codex is flexible to handle currently not foreseen situations. Nobody wants a future where are DL-based image/video compression tool only knows a few pre-determined sets and is mediocre on everything else.

Is this open source or something that you are aiming to license?

It is a joke paper as a marketing stunt for Silicon Valley. I would bet they could get it accepted to some journals / conferences too since it looks extremely convincing.

Are you asking me whether or not you couldn't find it in the PDF?

On the actual web page the first line of its abstract:

"Even though over 70% of internet traffic today is digital media, the way images and video are represented and transmitted has not evolved much in the past 20 years (apart from Pied Piper's Middle-Out algorithm)."

From the PDF:

https://arxiv.org/pdf/1705.05823.pdf

The end of Section 2.2. ML-based lossy image compression Right above 2.3. Generative Adversarial Networks

Theis et al. (2016) and Ball ́ e et al. (2016) quantize rather than binarize, and propose strategies to approximate the entropy of the quantized representation: this provides them with a proxy to penalize it. Finally, Pied Piper has recently claimed to employ ML techniques in its Middle-Out algorithm (Judge et al., 2016), although their nature is shrouded in mystery.

It isn't 'very impressive work', it is marketing for Silicon Valley (impressive marketing though).

Literally the first sentence of the linked page:

"Even though over 70% of internet traffic today is digital media, the way images and video are represented and transmitted has not evolved much in the past 20 years (apart from Pied Piper's Middle-Out algorithm)."

EDIT: This is embarrassing that not one person in this thread seems to have actually read any of the paper. Now with obvious evidence that this is fiction, people still don't want to believe it.