用量子神经网络提升隐式神经表示的压缩效率，显著改善图像重建质量。
Quantum Implicit Neural Compression
- 将量子神经网络引入隐式神经表示，利用其指数级表达能力增强压缩
- 在多个基准数据集上实现最高1.2dB的率失真性能提升
- 适合对高质量图像压缩有需求的研究者和开发者
基于隐式神经表示（INR）的信号压缩是一种新兴技术，可使用少量比特表示多媒体信号。尽管基于INR的压缩在低分辨率信号上能实现高质量重建，但小模型在高频细节还原方面表现较差。为提升压缩效率，本文提出量子隐式神经表示（quINR），利用量子神经网络的指数级丰富表达能力进行数据压缩。在若干基准数据集上的评估表明，所提出的quINR压缩方法在图像压缩中相比传统编码器和经典INR方法，率失真性能最高提升1.2dB。
原文摘要 · Abstract (English)
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.
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