129 citations · 159 across the 12 of their papers we have counts for
6 papers · 1 filter
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
Farhad Pakdaman, Moncef Gabbouj
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 app…
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs
Yuxin Xie, Li Yu, Farhad Pakdaman +1
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 hi…
Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss
Li Yu, Yanjun Gao, Farhad Pakdaman +1
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…
Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes
Umut Cem Entok, Firas Laakom, Farhad Pakdaman +1
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, pr…
Perceptual Learned Image Compression via End-to-End JND-Based Optimization
Farhad Pakdaman, Sanaz Nami, Moncef Gabbouj
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 th…
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
Ali Falahati, Mohammad Karim Safavi, Ardavan Elahi +2
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…