1 citations · 2 across the 5 of their papers we have counts for
5 papers
Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation
Bharath Bhushan Damodaran, Francois Schnitzler, Anne Lambert +1
Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR). In this paper, we propose a novel positi…
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…
RQAT-INR: Improved Implicit Neural Image Compression
Bharath Bhushan Damodaran, Muhammet Balcilar, Franck Galpin +1
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compress…
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…
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…