12 citations · 17 across the 23 of their papers we have counts for
25 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…
Cool-chic 5.0: Faster Encoding and Inter-Feature Entropy Modeling for Overfitted Image Compression
Théo Ladune, Pierrick Philippe, Pierre Jaffuer +4
Overfitted codecs compress an image by learning a decoder tailored to the content during the encoding. As such, they trade increased encoding complexity for strong compression perf…
HyperCool: Reducing Encoding Cost in Overfitted Codecs with Hypernetworks
Pep Borrell-Tatché, Till Aczel, Théo Ladune +1
Overfitted image codecs like Cool-chic achieve strong compression by tailoring lightweight models to individual images, but their encoding is slow and computationally expensive. To…
3DOF+Quantization: 3DGS quantization for large scenes with limited Degrees of Freedom
Matthieu Gendrin, Stéphane Pateux, Théo Ladune
3D Gaussian Splatting (3DGS) is a major breakthrough in 3D scene reconstruction. With a number of views of a given object or scene, the algorithm trains a model composed of 3D gaus…
Adam SLAM - the last mile of camera calibration with 3DGS
Matthieu Gendrin, Stéphane Pateux, Xiaoran Jiang +2
The quality of the camera calibration is of major importance for evaluating progresses in novel view synthesis, as a 1-pixel error on the calibration has a significant impact on th…
Efficient Sub-pixel Motion Compensation in Learned Video Codecs
Théo Ladune, Thomas Leguay, Pierrick Philippe +2
Motion compensation is a key component of video codecs. Conventional codecs (HEVC and VVC) have carefully refined this coding step, with an important focus on sub-pixel motion comp…
BOGausS: Better Optimized Gaussian Splatting
Stéphane Pateux, Matthieu Gendrin, Luce Morin +2
3D Gaussian Splatting (3DGS) proposes an efficient solution for novel view synthesis. Its framework provides fast and high-fidelity rendering. Although less complex than other solu…
Improved Encoding for Overfitted Video Codecs
Thomas Leguay, Théo Ladune, Pierrick Philippe +1
Overfitted neural video codecs offer a decoding complexity orders of magnitude smaller than their autoencoder counterparts. Yet, this low complexity comes at the cost of limited co…
Upsampling Improvement for Overfitted Neural Coding
Pierrick Philippe, Théo Ladune, Gordon Clare +3
Neural image compression, based on auto-encoders and overfitted representations, relies on a latent representation of the coded signal. This representation needs to be compact and…
Overfitted image coding at reduced complexity
Théophile Blard, Théo Ladune, Pierrick Philippe +3
Overfitted image codecs offer compelling compression performance and low decoder complexity, through the overfitting of a lightweight decoder for each image. Such codecs include Co…
Cool-chic video: Learned video coding with 800 parameters
Thomas Leguay, Théo Ladune, Pierrick Philippe +1
We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall. To the best of our knowledge, this is one of the neural video co…
Cool-Chic: Perceptually Tuned Low Complexity Overfitted Image Coder
Théo Ladune, Pierrick Philippe, Gordon Clare +2
This paper summarises the design of the Cool-Chic candidate for the Challenge on Learned Image Compression. This candidate attempts to demonstrate that neural coding methods can le…
ED: Perceptually tuned Enhanced Compression Model
Pierrick Philippe, Théo Ladune, Stéphane Davenet +1
This paper summarises the design of the candidate ED for the Challenge on Learned Image Compression 2024. This candidate aims at providing an anchor based on conventional coding te…
CAwa-NeRF: Instant Learning of Compression-Aware NeRF Features
Omnia Mahmoud, Théo Ladune, Matthieu Gendrin
Modeling 3D scenes by volumetric feature grids is one of the promising directions of neural approximations to improve Neural Radiance Fields (NeRF). Instant-NGP (INGP) introduced m…
Low-complexity Overfitted Neural Image Codec
Thomas Leguay, Théo Ladune, Pierrick Philippe +2
We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image. While autoencoders perform up to a million multiplications per deco…
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec
Théo Ladune, Pierrick Philippe, Félix Henry +2
We introduce COOL-CHIC, a Coordinate-based Low Complexity Hierarchical Image Codec. It is a learned alternative to autoencoders with 629 parameters and 680 multiplications per deco…
Artificial Intelligence based Video Codec (AIVC) for CLIC 2022
Théo Ladune, Gordon Clare, Pierrick Philippe +1
This paper presents the AIVC submission to the CLIC 2022 video track. AIVC is a fully-learned video codec based on conditional autoencoders. The flexibility of the AIVC models is l…
AIVC: Artificial Intelligence based Video Codec
Théo Ladune, Pierrick Philippe
This paper introduces AIVC, an end-to-end neural video codec. It is based on two conditional autoencoders MNet and CNet, for motion compensation and coding. AIVC learns to compress…
Coding Standards as Anchors for the CVPR CLIC video track
Théo Ladune, Pierrick Philippe
In 2021, a new track has been initiated in the Challenge for Learned Image Compression~: the video track. This category proposes to explore technologies for the compression of shor…
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…
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…