Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913245417177088 |
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| author | Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre |
| author_facet | Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre |
| contents | Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y. |
| title | Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2402.17470 |