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eess.IV2022★ 1 cited
Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression
Muhammet Balcilar, Bharath Damodaran, Pierre Hellier
End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. The core idea is to learn a non-lin…
eess.IV2022
Video Coding Using Learned Latent GAN Compression
Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao +1
We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intr…