3D-printed decoder, AI-enabled image compression could enable high-resolution displays

3D-printed decoder, AI-enabled image compression could enable high-resolution displays

AI optical decoder

image: the system uses an algorithm that encodes a high resolution image into a lower resolution image, then translates the compressed image to its original resolution by a decoder that descrambles the incoming light.
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Credit: Ozcan Lab/UCLA


A UCLA team has developed technology to project high-resolution computer-generated images using one-sixteenth the number of pixels contained in their source images. The system compresses the images based on an artificial intelligence algorithm, then decodes them using an optical decoder – a thin sheet of translucent plastic produced using a 3D printer – designed to interact with light in a specific way as part of the same algorithm. . The decoder does not consume power, which can result in higher resolution displays that consume less power and require less data than current display technologies.


Projecting high-resolution 3D holograms requires so many pixels that the task is beyond the reach of current consumer technology. The ability to compress image data and instantly decode the compressed images using a thin, transparent material that does not consume power, as demonstrated in the study, could help overcome this hurdle and achieve portable technology that produces higher quality images while using less power and storage. than today’s consumer technology.


The system uses an algorithm that encodes a high resolution image into a lower resolution image. The result is a pixelated pattern, similar to a QR code, which is unreadable to the human eye. This compressed image is then scaled back to its original resolution by a decoder designed to bend and decipher the incoming light.

By testing the system on black, white and grayscale images, the researchers demonstrated that the technology could effectively project high-resolution images using images encoded with only about 6% of the pixels of the original. The team also tested a similar system that successfully encoded and decoded color images.


The technology could eventually be used for applications such as the projection of high-resolution holographic images for virtual reality or augmented reality glasses. By encoding images using a fraction of the data contained in the original and decoding them without using electricity, the system could lead to smaller, cheaper holographic displays with faster refresh rates.

The technology could appear in consumer electronics within five years, according to the paper’s corresponding author, Aydogan Ozcan, Chancellor Professor of Electrical Engineering and Bioengineering, Volgenau Professor of Engineering Innovation at the UCLA Samueli School of Engineering and Associate Director of the California NanoSystems Institute at UCLA.

Other potential applications include image encryption and medical imaging.


The study’s co-first authors are UCLA PhD students Çağatay Işıl and Deniz Mengu. Mona Jarrahi, Northrop Grumman Professor of Electrical Engineering at UCLA, is co-lead author. The other authors are Yifan Zhao, Anika Tabassum, Jingxi Li and Yi Luo, all from UCLA.


The study is published in Science Advances.


The research was funded by the Department of Energy.

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