18 citations · 23 across the 9 of their papers we have counts for
10 papers
N-O Cool-chic: reconcile fast encoding with lightweight decoding for neural image compression
Théophile Blard, Théo Ladune, Pierrick Philippe +2
Overfitted image codecs achieve strong compression performance and low decoder complexity by learning a lightweight decoder for each image. Such codecs include Cool-chic, which pre…
Spatial Competition for Low-Complexity Learned Image Compression
Théophile Blard, Pierrick Philippe, Théo Ladune +2
Autoencoder-based image codecs achieve state-of-the-art compression performance but often incur high computational complexity, particularly at decoding time. This work introduces a…
Lightweight Hardware Transform Design for the Versatile Video Coding 4K ASIC Decoders
Ibrahim Farhat, Wassim Hamidouche, Adrien Grill +2
Versatile Video Coding (VVC) is the next generation video coding standard finalized in July 2020. VVC introduces new coding tools enhancing the coding efficiency compared to its pr…
Conditional Coding for Flexible Learned Video Compression
Théo Ladune, Pierrick Philippe, Wassim Hamidouche +2
This paper introduces a novel framework for end-to-end learned video coding. Image compression is generalized through conditional coding to exploit information from reference frame…
Conditional Coding and Variable Bitrate for Practical Learned Video Coding
Théo Ladune, Pierrick Philippe, Wassim Hamidouche +2
This paper introduces a practical learned video codec. Conditional coding and quantization gain vectors are used to provide flexibility to a single encoder/decoder pair, which is a…
Selective Encryption of the Versatile Video Coding Standard
Guillaume Gautier, Mousa FarajAllah, Wassim Hamidouche +2
Versatile video coding (VVC) is the next generation video coding standard developed by the joint video experts team (JVET) and released in July 2020. VVC introduces several new cod…
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
Ahmed Aldahdooh, Wassim Hamidouche, Olivier Déforges
Security-sensitive applications that rely on Deep Neural Networks (DNNs) are vulnerable to small perturbations that are crafted to generate Adversarial Examples(AEs). The AEs are i…
Optical Flow and Mode Selection for Learning-based Video Coding
Théo Ladune, Pierrick Philippe, Wassim Hamidouche +2
This paper introduces a new method for inter-frame coding based on two complementary autoencoders: MOFNet and CodecNet. MOFNet aims at computing and conveying the Optical Flow and…
ModeNet: Mode Selection Network For Learned Video Coding
Théo Ladune, Pierrick Philippe, Wassim Hamidouche +2
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression. Inspired by traditional video coding, ModeNet purpose is to enable c…
Light Field Image Coding Using Dual Discriminator Generative Adversarial Network and VVC Temporal Scalability
Nader Bakir, Wassim Hamidouche, Sid Fezza +2
Light field technology represents a viable path for providing a high-quality VR content. However, such an imaging system generates a high amount of data leading to an urgent need f…