Publications (6)
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
Jacob Granley, Lucas Relic, Michael Beyeler
Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often…
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression
Lucas Relic, Roberto Azevedo, Yang Zhang +2
Generative neural image compression supports data representation at extremely low bitrate, synthesizing details at the client and consistently producing highly realistic images. By…
DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning
Eren Ãetin, Lucas Relic, Yuanyi Xue +3
We present a perceptually-driven video compression framework integrating implicit neural representations (INRs) and pre-trained video diffusion models to address the extremely low…
Region-Adaptive Generative Compression with Spatially Varying Diffusion Models
Lucas Relic, Roberto Azevedo, Yang Zhang +3
Generative image codecs aim to optimize perceptual quality, producing realistic and detailed reconstructions. However, they often overlook a key property of human vision: our tende…
Lossy Image Compression with Foundation Diffusion Models
Lucas Relic, Roberto Azevedo, Markus Gross +1
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates. Previous m…
Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
Lucas Relic, Bowen Zhang, Yi-Lin Tuan +1
Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifyin…