19 citations · 19 across the 3 of their papers we have counts for
3 papers
eess.IV2026
DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning
Eren Çetin, 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…
eess.IV2026
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…
cs.CV2018★ 19 cited
Deep Generative Video Compression
Jun Han, Salvator Lombardo, Christopher Schroers +1
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here…