Zobrazeno 1 - 10
of 2 392
pro vyhledávání: '"A, Pakdaman"'
Autor:
Pakdaman, Farhad, Gabbouj, Moncef
Publikováno v:
IEEE Signal Processing Letters, 2024
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This approach is considered as the future of image/video compression, and majo
Externí odkaz:
http://arxiv.org/abs/2403.10936
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as high-frequency signals, its presence leads to extra bits needed for comp
Externí odkaz:
http://arxiv.org/abs/2402.05582
Publikováno v:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish valid pixels from invalid pixels and find suitable references for cor
Externí odkaz:
http://arxiv.org/abs/2402.02936
Most scenes are illuminated by several light sources, where the traditional assumption of uniform illumination is invalid. This issue is ignored in most color constancy methods, primarily due to the complex spatial impact of multiple light sources on
Externí odkaz:
http://arxiv.org/abs/2402.02922
Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of this approach over traditional codecs is that any optimization criteria
Externí odkaz:
http://arxiv.org/abs/2402.02836
Publikováno v:
Proc. 2024 13th Iranian/3rd Int. Conf. Mach. Vis. Image Process. (MVIP) (2024) 1-7
Providing high-quality video with efficient bitrate is a main challenge in video industry. The traditional one-size-fits-all scheme for bitrate ladders is inefficient and reaching the best content-aware decision computationally impractical due to ext
Externí odkaz:
http://arxiv.org/abs/2401.03195
Autor:
Mahdi Pakdaman, Yaghoub Tadi Beni
Publikováno v:
Journal of Applied and Computational Mechanics, Vol 11, Iss 1, Pp 223-238 (2025)
Nowadays, there has been a notable surge in the utilization of piezoelectric materials at the micro and nano scales, manifesting across various branches of science through the development of diverse microstructures. On the other hand, given the deplo
Externí odkaz:
https://doaj.org/article/5b13c04941ce4f579653a94876c4ba5b
Autor:
Satar Rezaei, Maryam Karimi, Shahin Soltani, Eshagh Barfar, Mohammad Ali Mohammadi Gharehghani, Abbas Badakhshan, Nasim Badiee, Mohsen Pakdaman, Heather Brown
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background One of the key functions and ultimate goals of health systems is to provide financial protection for individuals when using health services. This study sought to evaluate the level of financial protection and its inequality among
Externí odkaz:
https://doaj.org/article/402c92062473475fb2e17b44c1c22624
Publikováno v:
Global Pediatric Health, Vol 11 (2024)
Working with chronically ill children can overwhelm for professionals. It is necessary to study the factors related to compassion fatigue and satisfaction in order to effectively deal with it. Using a narrative review and inclusion criteria, we searc
Externí odkaz:
https://doaj.org/article/9cb04e7991f34f56acd27e4bb6f8f673
Autor:
Pakdaman, Farhad, Gabbouj, Moncef
Publikováno v:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023
Learned Compression (LC) is the emerging technology for compressing image and video content, using deep neural networks. Despite being new, LC methods have already gained a compression efficiency comparable to state-of-the-art image compression, such
Externí odkaz:
http://arxiv.org/abs/2212.05466