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20182020
most citedNeural Image Compression via Non-Local Attention Optimization and Improved Context Modeling

325 citations · 969 across the 12 of their papers we have counts for

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7 papers · 1 filter

eess.IV2019★ 325 cited

Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling

Tong Chen, Haojie Liu, Zhan Ma +3

This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual networ…

eess.IV2019★ 1 cited

Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications

Ming Lu, Ming Cheng, Yiling Xu +3

Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have de…

eess.IV2019★ 321 cited

Non-local Attention Optimized Deep Image Compression

Haojie Liu, Tong Chen, Peiyao Guo +4

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. O…

eess.IV2019★ 3 cited

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

Chao Huang, Haojie Liu, Tong Chen +2

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "prior…

eess.IV2019★ 8 cited

Gated Context Model with Embedded Priors for Deep Image Compression

Haojie Liu, Tong Chen, Peiyao Guo +2

A deep image compression scheme is proposed in this paper, offering the state-of-the-art compression efficiency, against the traditional JPEG, JPEG2000, BPG and those popular learn…

eess.IV2019★ 10 cited

Neural Video Compression using Spatio-Temporal Priors

Haojie Liu, Tong Chen, Ming Lu +2

The pursuit of higher compression efficiency continuously drives the advances of video coding technologies. Fundamentally, we wish to find better "predictions" or "priors" that are…

eess.IV2018★ 34 cited

Deep Image Compression via End-to-End Learning

Haojie Liu, Tong Chen, Qiu Shen +2

We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-sc…