频率感知重参数化用于基于过拟合的图像压缩
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression
AI总结：
针对基于过拟合的图像压缩，提出一种频率感知重参数化方法，将卷积核表示为DCT核的加权和，结合L1正则化，在频域直接优化，实现低计算成本下显著改善率失真，仅200次迭代即在HEIF基础上获得高达-46.12%的BD-rate提升。
摘要：
基于过拟合的图像压缩要求权重紧凑性以实现压缩，并要求快速收敛以用于实际应用，这对基于深度卷积神经网络（CNN）的方法提出了挑战。本文提出了一种简单的重参数化方法，以训练具有减少权重存储和加速收敛的CNN。卷积核被重参数化为离散余弦变换（DCT）核的加权和，从而能够在频域中直接优化。结合L1正则化，所提出的方法超越了普通卷积，以较低的计算成本实现了显著改善的率失真性能。该方法通过在不同数据集上进行的基于过拟合的图像恢复的大量实验得到验证，在仅200次迭代的情况下，在HEIF基础上实现了高达-46.12%的BD-rate提升。
英文摘要：
Over-fitting-based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural networks (CNNs) based methods. This paper presents a simple re-parameterization method to train CNNs with reduced weights storage and accelerated convergence. The convolution kernels are re-parameterized as a weighted sum of discrete cosine transform (DCT) kernels enabling direct optimization in the frequency domain. Combined with L1 regularization, the proposed method surpasses vanilla convolutions by achieving a significantly improved rate-distortion with low computational cost. The proposed method is verified with extensive experiments of over-fitting-based image restoration on various datasets, achieving up to -46.12% BD-rate on top of HEIF with only 200 iterations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
