Quantized Decoder in Learned Image Compression for Deterministic Reconstruction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917564292005888 |
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| author | Koyuncu, Esin Solovyev, Timofey Sauer, Johannes Alshina, Elena Kaup, André |
| author_facet | Koyuncu, Esin Solovyev, Timofey Sauer, Johannes Alshina, Elena Kaup, André |
| contents | Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices. This paper shows a method to achieve a deterministic reconstructed image by quantizing only the decoder of the learned image compression model. From the implementation perspective of an image codec, it is beneficial to have the results reproducible when decoded on different devices. In this paper, we study quantization of weights and activations without overflow of accumulator in all decoder subnetworks. We show that the results are bit-exact at the output, and the resulting BD-rate loss of quantization of decoder is 0.5 % in the case of 16-bit weights and 16-bit activations, and 7.9 % in the case of 8-bit weights and 16-bit activations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11209 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantized Decoder in Learned Image Compression for Deterministic Reconstruction Koyuncu, Esin Solovyev, Timofey Sauer, Johannes Alshina, Elena Kaup, André Image and Video Processing Learned image compression has a problem of non-bit-exact reconstruction due to different calculations of floating point arithmetic on different devices. This paper shows a method to achieve a deterministic reconstructed image by quantizing only the decoder of the learned image compression model. From the implementation perspective of an image codec, it is beneficial to have the results reproducible when decoded on different devices. In this paper, we study quantization of weights and activations without overflow of accumulator in all decoder subnetworks. We show that the results are bit-exact at the output, and the resulting BD-rate loss of quantization of decoder is 0.5 % in the case of 16-bit weights and 16-bit activations, and 7.9 % in the case of 8-bit weights and 16-bit activations. |
| title | Quantized Decoder in Learned Image Compression for Deterministic Reconstruction |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2312.11209 |