| Autor: |
Gao, Fangyuan, Deng, Xin, Jing, Junpeng, Zou, Xin, Xu, Mai |
| Zdroj: |
IEEE Transactions on Circuits and Systems for Video Technology; August 2024, Vol. 34 Issue: 8 p6993-7004, 12p |
| Abstrakt: |
Image compression at extremely low bit-rates has always been a challenging task in bandwidth limited scenarios, such as aerospace and deep-sea explorations. Recent years have seen great success of deep learning in image compression, however, few of them are specially designed for extremely low bit-rate conditions. To solve this issue, in this paper, we propose a novel invertible image generation based framework for extremely low bit-rate image compression. The proposed framework is composed of three modules, including an invertible image generation (IIG) module, a generated image compression (GIC) module and a compressed image adjustment (CIA) module. The role of IIG module is to generate a compression-friendly image from the original image. In the IIG module, image generation and restoration are modelled as two mutually reversible processes to avoid the information loss. After the IIG module, the GIC module is employed to compress the generated images to save the coding bit-rates. After that, the CIA module is used to shrink the quality gap between the compressed generated image and the un-compressed image. Finally, the image from the CIA module is sent back to the IIG module to restore the original image. The experimental results on three different datasets show that the proposed framework achieves state-of-the-art performance in image compression with extremely low bit-rates. We also extend the proposed framework to feature compression towards object detection, which saves 90% bit-rates than the VVC standard with the same detection accuracy. |
| Databáze: |
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