3 citations · 5 across the 15 of their papers we have counts for
15 papers
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…
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…
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…
Video Quality Assessment and Coding Complexity of the Versatile Video Coding Standard
Thomas Amestoy, Naty Sidaty, Wassim Hamidouche +2
In recent years, the proliferation of multimedia applications and formats, such as IPTV, Virtual Reality (VR, 360-degree), and point cloud videos, has presented new challenges to t…
Efficient Predictive Coding of Intra Prediction Modes
Kevin Reuzé, Wassim Hamidouche, Pierrick Philippe +1
The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential f…
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…
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…