In this paper, we explicitly analyze the performance effects of several orthogonal and bi-orthogonal wavelet families. For each family, we explore the impact of the filter order (length) and the decomposition depth in the multiresolution representation. In particular, two contexts of use are examined: compression and denoising. In both cases, the experiments are carried out on a large dataset of different signal kinds, including various image sets and 1D signals (audio, electrocardiogram and seismic). Results for all the considered wavelets are shown on each dataset. Collectively, the study suggests that a meticulous choice of wavelet parameters significantly alters the performance of the above mentioned tasks. To the best of authors’ knowledge, this work represents the most complete analysis and comparison between wavelet filters. Therefore, it represents a valuable benchmark for future works.

在本文中，我们明确分析了几种正交和双正交小波族的性能影响。对于每个家庭，我们都将探讨滤波器分辨率（长度）和分解深度在多分辨率表示中的影响。特别是，检查了两个使用上下文：压缩和降噪。在这两种情况下，实验都是在不同信号类型的大型数据集上进行的，包括各种图像集和一维信号（音频，心电图和地震信号）。所有考虑的小波的结果都显示在每个数据集上。总体而言，该研究表明，精心选择的小波参数会极大地改变上述任务的性能。据作者所知，这项工作代表了小波滤波器之间最完整的分析和比较。因此，