9 citations · 11 across the 7 of their papers we have counts for
9 papers
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression
Ahmed Ghorbel, Wassim Hamidouche, Luce Morin
Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their convent…
AICT: An Adaptive Image Compression Transformer
Ahmed Ghorbel, Wassim Hamidouche, Luce Morin
Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightfo…
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression
Ahmed Ghorbel, Wassim Hamidouche, Luce Morin
Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs.…
Ensemble Learning for Efficient VVC Bitrate Ladder Prediction
Fatemeh Nasiri, Wassim Hamidouche, Luce Morin +2
Changing the encoding parameters, in particular the video resolution, is a common practice before transcoding. To this end, streaming and broadcast platforms benefit from so-called…
Prediction-Aware Quality Enhancement of VVC Using CNN
Fatemeh Nasiri, Wassim Hamidouche, Luce Morin +2
The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate…
A CNN-based Prediction-Aware Quality Enhancement Framework for VVC
Fatemeh Nasiri, Wassim Hamidouche, Luce Morin +2
This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The…