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20182021
most citedDeep Image Compression via End-to-End Learning

34 citations · 67 across the 13 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

eess.IV2019★ 3 cited

Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

Haojie Liu, Han shen, Lichao Huang +3

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role i…

cs.CV2019

PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation

Haojie Liu, Kang Liao, Chunyu Lin +2

LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10Hz) and have been widely applied in the field of autonomous driving and UAV. However, the c…

eess.IV2019★ 1 cited

A Dual Camera System for High Spatiotemporal Resolution Video Acquisition

Ming Cheng, Zhan Ma, M. Salman Asif +4

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rat…

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…