no code implementations • 28 Feb 2024 • Ahmed Ghorbel, Wassim Hamidouche, Luce Morin
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint.
no code implementations • 12 Jul 2023 • 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 straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT).
no code implementations • 12 Jul 2023 • 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 conventional counterparts in rate-distortion performance.
no code implementations • 5 Jul 2023 • 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.
1 code implementation • 5 Jul 2022 • Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni, Wassim Hamidouche
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload.