11 citations · 14 across the 3 of their papers we have counts for
3 papers
eess.IV2020
Variable Rate Image Compression Method with Dead-zone Quantizer
Jing Zhou, Akira Nakagawa, Keizo Kato +3
Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO…
eess.IV2019★ 11 cited
Multi-scale and Context-adaptive Entropy Model for Image Compression
Jing Zhou, Sihan Wen, Akira Nakagawa +2
We propose an end-to-end trainable image compression framework with a multi-scale and context-adaptive entropy model, especially for low bitrate compression. Due to the success of…
cs.LG2019★ 3 cited
Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space
Keizo Kato, Jing Zhou, Tomotake Sasaki +1
To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) a…