Towards Lossless Implicit Neural Representation via Bit Plane Decomposition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916657722556416 |
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| author | Han, Woo Kyoung Lee, Byeonghun Cho, Hyunmin Im, Sunghoon Jin, Kyong Hwan |
| author_facet | Han, Woo Kyoung Lee, Byeonghun Cho, Hyunmin Im, Sunghoon Jin, Kyong Hwan |
| contents | We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_21001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Lossless Implicit Neural Representation via Bit Plane Decomposition Han, Woo Kyoung Lee, Byeonghun Cho, Hyunmin Im, Sunghoon Jin, Kyong Hwan Computer Vision and Pattern Recognition We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bound of the model size. We validate our hypothesis that reducing the upper bound leads to faster convergence with constant model size. Our method achieves lossless representation in 2D image and audio fitting, even for high bit-depth signals, such as 16-bit, which was previously unachievable. We pioneered the presence of bit bias, which INR prioritizes as the most significant bit (MSB). We expand the application of the INR task to bit depth expansion, lossless image compression, and extreme network quantization. Our source code is available at https://github.com/WooKyoungHan/LosslessINR |
| title | Towards Lossless Implicit Neural Representation via Bit Plane Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.21001 |