28 citations · 69 across the 6 of their papers we have counts for
6 papers
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
Haisheng Fu, Feng Liang, Jianping Lin +7
Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versat…
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Mohammad Akbari, Jie Liang, Jingning Han +1
Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rat…
Learned Variable-Rate Multi-Frequency Image Compression using Modulated Generalized Octave Convolution
Jianping Lin, Mohammad Akbari, Haisheng Fu +7
In this proposal, we design a learned multi-frequency image compression approach that uses generalized octave convolutions to factorize the latent representations into high-frequen…
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
Mohammad Akbari, Jie Liang, Jingning Han +1
Learned image compression has recently shown the potential to outperform the standard codecs. State-of-the-art rate-distortion (R-D) performance has been achieved by context-adapti…
Learned Variable-Rate Image Compression with Residual Divisive Normalization
Mohammad Akbari, Jie Liang, Jingning Han +1
Recently it has been shown that deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networ…
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Haisheng Fu, Feng Liang, Bo Lei +5
Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compress…