Quantum Implicit Neural Compression
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912170569105408 |
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| author | Fujihashi, Takuya Koike-Akino, Toshiaki |
| author_facet | Fujihashi, Takuya Koike-Akino, Toshiaki |
| contents | Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly degraded with a small model. To improve the compression efficiency of INR, we introduce quantum INR (quINR), which leverages the exponentially rich expressivity of quantum neural networks for data compression. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods, up to 1.2dB gain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Implicit Neural Compression Fujihashi, Takuya Koike-Akino, Toshiaki Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantum Algebra Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly degraded with a small model. To improve the compression efficiency of INR, we introduce quantum INR (quINR), which leverages the exponentially rich expressivity of quantum neural networks for data compression. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods, up to 1.2dB gain. |
| title | Quantum Implicit Neural Compression |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantum Algebra |
| url | https://arxiv.org/abs/2412.19828 |