Zobrazeno 1 - 5
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pro vyhledávání: '"Jia, Panqi"'
Autor:
Jia, Panqi, Koyuncu, A. Burakhan, Mao, Jue, Cui, Ze, Ma, Yi, Guo, Tiansheng, Solovyev, Timofey, Karabutov, Alexander, Zhao, Yin, Wang, Jing, Alshina, Elena, Kaup, Andre
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transform
Externí odkaz:
http://arxiv.org/abs/2402.17487
Autor:
Jia, Panqi, Mao, Jue, Koyuncu, Esin, Koyuncu, A. Burakhan, Solovyev, Timofey, Karabutov, Alexander, Zhao, Yin, Alshina, Elena, Kaup, Andre
Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transform
Externí odkaz:
http://arxiv.org/abs/2402.17470
Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achieving a high compression ratio, however, at the expense of a significa
Externí odkaz:
http://arxiv.org/abs/2306.14287
Autor:
Jia, Panqi, Koyuncu, Ahmet Burakhan, Gaikov, Georgii, Karabutov, Alexander, Alshina, Elena, Kaup, Andre
Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel da
Externí odkaz:
http://arxiv.org/abs/2212.06241
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology; August 2024, Vol. 34 Issue: 8 p7498-7511, 14p