38 citations · 73 across the 10 of their papers we have counts for
4 papers · 1 filter
Latent-Shift: Gradient of Entropy Helps Neural Codecs
Muhammet Balcilar, Bharath Bhushan Damodaran, Karam Naser +2
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These t…
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
Franck Galpin, Muhammet Balcilar, Frédéric Lefebvre +2
End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are…
Improving The Reconstruction Quality by Overfitted Decoder Bias in Neural Image Compression
Oussama Jourairi, Muhammet Balcilar, Anne Lambert +1
End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned ov…
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…