Kavli Affiliate: Jing Wang
| First 5 Authors: Meng Li, Shangyin Gao, Yihui Feng, Yibo Shi, Jing Wang
| Summary:
In recent years, with the development of deep neural networks, end-to-end
optimized image compression has made significant progress and exceeded the
classic methods in terms of rate-distortion performance. However, most
learning-based image compression methods are unlabeled and do not consider
image semantics or content when optimizing the model. In fact, human eyes have
different sensitivities to different content, so the image content also needs
to be considered. In this paper, we propose a content-oriented image
compression method, which handles different kinds of image contents with
different strategies. Extensive experiments show that the proposed method
achieves competitive subjective results compared with state-of-the-art
end-to-end learned image compression methods or classic methods.
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