12 citations · 17 across the 23 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
Binary Probability Model for Learning Based Image Compression
Théo Ladune, Pierrick Philippe, Wassim Hamidouche +2
In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables. Previous works model the latents with a Gaussian or…