Search Results for author:
Found 9 papers, 1 papers with code
Designs and Implementations in Neural Network-based Video Coding
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language model like ChatGPT.
Latent-Shift: Gradient of Entropy Helps Neural Codecs
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts.
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications.
RQAT-INR: Improved Implicit Neural Image Compression
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC.
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks
The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at block boundaries specifically.
Neural Network based Inter bi-prediction Blending
This paper presents a learning-based method to improve bi-prediction in video coding.
Combined neural network-based intra prediction and transform selection
The interactions between different tools added successively to a block-based video codec are critical to its rate-distortion efficiency.
Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders
This new approach leads to five possible splits at each block depth and thereby improves the coding efficiency of VVC over that of the preceding High Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT) partitioning with a single split per block depth.
Iterative training of neural networks for intra prediction
This paper presents an iterative training of neural networks for intra prediction in a block-based image and video codec.
