Humans compress images better than algorithms, experiment finds
Your friend texts you a photo of the dog she's about to adopt but all you see is a tan, vaguely animal-shaped haze of pixels. To get you a bigger picture, she sends the link to the dog's adoption profile because she's worried about her data limit. One click and your screen fills with much more satisfying descriptions and images of her best-friend-to-be.
Source: Taylor Kubota
Sending a link instead of uploading a massive image is just one trick humans use to convey information without burning through data. In fact, these tricks might inspire an entirely new class of image compression algorithms, according to research by a team of Stanford Univeristy engineers and high school students.
The researchers asked people to compare images produced by a traditional compression algorithm that shrink huge images into pixilated blurs to those created by humans in data-restricted conditions – text-only communication, which could include links to public images. In many cases, the products of human-powered image sharing proved more satisfactory than the algorithm's work. The researchers will present their work March 28 at the 2019 Data Compression Conference.
"Almost every image compressor we have today is evaluated using metrics that don't necessarily represent what humans value in an image," said Irena Fischer-Hwang, a graduate student in electrical engineering and co-author of the paper. "It turns out our algorithms have a long way to go and can learn a lot from the way humans share information."
The project resulted from a collaboration between researchers led by Tsachy Weissman, professor of electrical engineering, and three high school students who interned in his lab.
"Honestly, we came into this collaboration aiming to give the students something that wouldn't distract too much from ongoing research," said Weissman. "But they wanted to do more, and that chutzpah led to a paper and a whole new research thrust for the group. This could very well become among the most exciting projects I've ever been involved in."
Converting images into a compressed format, such as a JPEG, makes them significantly smaller, but loses some detail – this form of conversion is often called "lossy" for that reason. The resulting image is lower quality because the algorithm has to sacrifice details about color and luminance in order to consume less data. Although the algorithms retain enough detail for most cases, Weissman's interns thought they could do better.