8 papers
Enhanced Neural Video Representation Compression across Extreme Complexity and Quality Scales
Ho Man Kwan, Tianhao Peng, Fan Zhang +3
Implicit neural representations (INRs) have recently emerged as a promising approach to video compression, delivering competitive rate-distortion performance alongside rapid decodi…
High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning
Siyue Teng, Ho Man Kwan, Yuxuan Jiang +2
Learning-based video compression has recently achieved competitive rate-distortion performance compared to conventional video codecs. However, most existing methods rely on non-inv…
Instance Data Condensation for Image Super-Resolution
Tianhao Peng, Ho Man Kwan, Yuxuan Jiang +5
Deep learning based Image Super-Resolution (ISR) relies on large training datasets to optimize model generalization; this requires substantial computational and storage resources d…
Ultra-lightweight Neural Video Representation Compression
Ho Man Kwan, Tianhao Peng, Ge Gao +4
Recent works have demonstrated the viability of utilizing over-fitted implicit neural representations (INRs) as alternatives to autoencoder-based models for neural video compressio…
NVRC: Neural Video Representation Compression
Ho Man Kwan, Ge Gao, Fan Zhang +2
Recent advances in implicit neural representation (INR)-based video coding have demonstrated its potential to compete with both conventional and other learning-based approaches. Wi…
ViVo: A Dataset for Volumetric Video Reconstruction and Compression
Adrian Azzarelli, Ge Gao, Ho Man Kwan +4
As research on neural volumetric video reconstruction and compression flourishes, there is a need for diverse and realistic datasets, which can be used to develop and validate reco…