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Found 15 papers, 7 papers with code
Overfitted image coding at reduced complexity
Such codecs include Cool-chic, which presents image coding performance on par with VVC while requiring around 2000 multiplications per decoded pixel.
Cool-chic video: Learned video coding with 800 parameters
We propose a lightweight learned video codec with 900 multiplications per decoded pixel and 800 parameters overall.
Cool-Chic: Perceptually Tuned Low Complexity Overfitted Image Coder
This paper summarises the design of the Cool-Chic candidate for the Challenge on Learned Image Compression.
ED: Perceptually tuned Enhanced Compression Model
This paper summarises the design of the candidate ED for the Challenge on Learned Image Compression 2024.
CAwa-NeRF: Instant Learning of Compression-Aware NeRF Features
Modeling 3D scenes by volumetric feature grids is one of the promising directions of neural approximations to improve Neural Radiance Fields (NeRF).
Low-complexity Overfitted Neural Image Codec
We propose a neural image codec at reduced complexity which overfits the decoder parameters to each input image.
COOL-CHIC: Coordinate-based Low Complexity Hierarchical Image Codec
At the receiver side, the compressed image is obtained by evaluating the mapping function for all pixel coordinates.
Artificial Intelligence based Video Codec (AIVC) for CLIC 2022
This paper presents the AIVC submission to the CLIC 2022 video track.
AIVC: Artificial Intelligence based Video Codec
This paper introduces AIVC, an end-to-end neural video codec.
Coding Standards as Anchors for the CVPR CLIC video track
With this in mind, this paper documents how to generate the video sequences fulfilling the requirements of this challenge, in a reproducible way, targeting the maximum performance for VVC.
Conditional Coding and Variable Bitrate for Practical Learned Video Coding
This paper introduces a practical learned video codec.
Conditional Coding for Flexible Learned Video Compression
This paper introduces a novel framework for end-to-end learned video coding.
Optical Flow and Mode Selection for Learning-based Video Coding
MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection.
ModeNet: Mode Selection Network For Learned Video Coding
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression.
Binary Probability Model for Learning Based Image Compression
In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables.
