Image Coding For Machines: an End-To-End Learned Approach

Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images. Given the dramatic explosion in the number of images generated per day, a question arises: how much better wou...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1590 - 1594
Hlavní autoři: Le, Nam, Zhang, Honglei, Cricri, Francesco, Ghaznavi-Youvalari, Ramin, Rahtu, Esa
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.06.2021
Témata:
Artificial neural networks
Codecs
Computer vision
Image coding
image coding for machines
image compression
loss weighting
multitask learning
Speech coding
Training
Video coding
video coding for machines
ISSN:2379-190X
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Shrnutí:Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images. Given the dramatic explosion in the number of images generated per day, a question arises: how much better would an image codec targeting machine-consumption perform against state-of-the-art codecs targeting human-consumption? In this paper, we propose an image codec for machines which is neural network (NN) based and end-to-end learned. In particular, we propose a set of training strategies that address the delicate problem of balancing competing loss functions, such as computer vision task losses, image distortion losses, and rate loss. Our experimental results show that our NN-based codec outperforms the state-of-the-art Versa-tile Video Coding (VVC) standard on the object detection and instance segmentation tasks, achieving -37.87% and -32.90% of BD-rate gain, respectively, while being fast thanks to its compact size. To the best of our knowledge, this is the first end-to-end learned machine-targeted image codec.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414465