Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Tu, Chengjie"'
Autor:
Fu, Haisheng, Liang, Feng, Lin, Jianping, Li, Bing, Akbari, Mohammad, Liang, Jie, Zhang, Guohe, Liu, Dong, Tu, Chengjie, Han, Jingning
Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key compo
Externí odkaz:
http://arxiv.org/abs/2107.06463
Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2012.15463
Autor:
Lin, Jianping, Akbari, Mohammad, Fu, Haisheng, Zhang, Qian, Wang, Shang, Liang, Jie, Liu, Dong, Liang, Feng, Zhang, Guohe, Tu, Chengjie
In this proposal, we design a learned multi-frequency image compression approach that uses generalized octave convolutions to factorize the latent representations into high-frequency (HF) and low-frequency (LF) components, and the LF components have
Externí odkaz:
http://arxiv.org/abs/2009.13074
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-adaptive entropy coding approaches in which hyperprior and autoregressive mo
Externí odkaz:
http://arxiv.org/abs/2002.10032
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 networks for multiple bit rates, which increases the implementation complexi
Externí odkaz:
http://arxiv.org/abs/1912.05688
Autor:
Fu, Haisheng, Liang, Feng, Lei, Bo, Bian, Nai, zhang, Qian, Akbari, Mohammad, Liang, Jie, Tu, Chengjie
Publikováno v:
Volume 82, March 2020, 115774
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 compression framework by combining deep learning and the traditional image cod
Externí odkaz:
http://arxiv.org/abs/1907.06566
Autor:
Tu, Chengjie, Zhang, Yuxin, Zhu, Peipei, Sun, Liuwei, Xu, Pei, Wang, Tianjing, Luo, Jing, Yu, Jingya, Xu, Letian
Publikováno v:
In Pesticide Biochemistry and Physiology June 2023 193
Publikováno v:
Integrative Zoology; Nov2024, Vol. 19 Issue 6, p1092-1104, 13p
Publikováno v:
In Signal Processing: Image Communication July 2021 95
Autor:
Fu, Haisheng, Liang, Feng, Lei, Bo, Bian, Nai, Zhang, Qian, Akbari, Mohammad, Liang, Jie, Tu, Chengjie
Publikováno v:
In Signal Processing: Image Communication March 2020 82