6 citations · 7 across the 5 of their papers we have counts for
5 papers
Wavefront Parallelization for Efficient Learned Image Compression
Shimon Murai, Fangzheng Lin, Kasidis Arunruangsirilert +1
Autoregressive context models are foundational for learned image compression,but they suffer from slow serial inference. Existing acceleration methods such as checkerboard context…
FlashGMM: Fast Gaussian Mixture Entropy Model for Learned Image Compression
Shimon Murai, Fangzheng Lin, Jiro Katto
High-performance learned image compression codecs require flexible probability models to fit latent representations. Gaussian Mixture Models (GMMs) were proposed to satisfy this de…
Real-time Video Prediction With Fast Video Interpolation Model and Prediction Training
Shota Hirose, Kazuki Kotoyori, Kasidis Arunruangsirilert +3
Transmission latency significantly affects users' quality of experience in real-time interaction and actuation. As latency is principally inevitable, video prediction can be utiliz…
Recoil: Parallel rANS Decoding with Decoder-Adaptive Scalability
Fangzheng Lin, Kasidis Arunruangsirilert, Heming Sun +1
Entropy coding is essential to data compression, image and video coding, etc. The Range variant of Asymmetric Numeral Systems (rANS) is a modern entropy coder, featuring superior s…
Multistage Spatial Context Models for Learned Image Compression
Fangzheng Lin, Heming Sun, Jinming Liu +1
Recent state-of-the-art Learned Image Compression methods feature spatial context models, achieving great rate-distortion improvements over hyperprior methods. However, the autoreg…