37 citations · 101 across the 14 of their papers we have counts for
16 papers
Bridging the gap between image coding for machines and humans
Nam Le, Honglei Zhang, Francesco Cricri +5
Image coding for machines (ICM) aims at reducing the bitrate required to represent an image while minimizing the drop in machine vision analysis accuracy. In many use cases, such a…
Leveraging progressive model and overfitting for efficient learned image compression
Honglei Zhang, Francesco Cricri, Hamed Rezazadegan Tavakoli +2
Deep learning is overwhelmingly dominant in the field of computer vision and image/video processing for the last decade. However, for image and video compression, it lags behind th…
Adaptation and Attention for Neural Video Coding
Nannan Zou, Honglei Zhang, Francesco Cricri +6
Neural image coding represents now the state-of-the-art image compression approach. However, a lot of work is still to be done in the video domain. In this work, we propose an end-…
Lossless Image Compression Using a Multi-Scale Progressive Statistical Model
Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli +3
Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques,…
Learning to Learn to Compress
Nannan Zou, Honglei Zhang, Francesco Cricri +5
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural networ…
End-to-End Learning for Video Frame Compression with Self-Attention
Nannan Zou, Honglei Zhang, Francesco Cricri +5
One of the core components of conventional (i.e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In…