用张量压缩技术降低图像存储与传输的内存和能耗。
tenSVD algorithm for compression
- 将图像转为高阶张量，用Tucker模型实现高效压缩。
- 相比基线算法，计算时间更短且信息保留率更高。
- 适合关注能效与数据压缩效率的研究者。
张量为处理高维数据提供了稳健框架，因此张量分析已成为机器学习、信号处理、计算机视觉、图分析和数据挖掘等多个领域的研究热点。本文提出一种基于张量的高效图像存储方法，旨在最小化存储内存、传输带宽和处理能耗。该方法将原始数据组织为高阶张量，并采用Tucker模型进行压缩。在R语言中实现，并与基线算法对比。评估聚焦于算法效率（计算时间）和信息保留质量，使用模拟和真实数据集。通过定量指标进行详细分析，特别关注不同算法在能源消耗方面的可持续性表现。
原文摘要 · Abstract (English)
Tensors provide a robust framework for managing high-dimensional data. Consequently, tensor analysis has emerged as an active research area in various domains, including machine learning, signal processing, computer vision, graph analysis, and data mining. This study introduces an efficient image storage approach utilizing tensors, aiming to minimize memory to store, bandwidth to transmit and energy to processing. The proposed method organizes original data into a higher-order tensor and applies the Tucker model for compression. Implemented in R, this method is compared to a baseline algorithm. The evaluation focuses on efficient of algorithm measured in term of computational time and the quality of information preserved, using both simulated and real datasets. A detailed analysis of the results is conducted, employing established quantitative metrics, with significant attention paid to sustainability in terms of energy consumption across algorithms.
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