I am a senior engineer at Qualcomm AI Research in San Diego, working on 3D computer vision and machine learning. I received my PhD in EE from the University of Notre Dame under Dr. Nicholas Zabaras on deep learning for modeling PDE systems. I studied automatic control at Tongji University.
I'm interested in high-quality 3D reconstruction, neural rendering, and generative design. I also worked on data compression.
yinhaozh@gmail.com
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github
AFAIK SwinT-ChARM is the first neural image codec that outperforms VTM in rate-distortion while with comparable decoding time on GPU.
An embedded bitstream is obtained with nested quantization and per-element sorting by prior stddev, based on the hyperprior model.
PReLU can replace GDN in the hyperprior model to compress YUV (and RGB!) images without loss of coding gain.