标题： 量子隐式神经压缩 显示英文标题

标题： Quantum Implicit Neural Compression

摘要： 基于隐式神经表示 (INR) 的信号压缩是一种以少量位表示多媒体信号的新兴技术。虽然基于 INR 的信号压缩在相对低分辨率的信号重建方面表现出色，但当模型较小时，高频细节的准确性会显著下降。 为了提高 INR 的压缩效率，我们引入了量子 INR（quINR），利用量子神经网络的指数级丰富的表达能力来进行数据压缩。 使用一些基准数据集的评估表明，与传统编解码器和经典的基于 INR 的编码方法相比，基于所提出的 quINR 的压缩方法在图像压缩中的率失真性能可提升高达 1.2 dB。 显示英文摘要

摘要： 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.