tenSVD algorithm for compression

TL;DR

This paper presents tenSVD, a tensor-based image compression algorithm using the Tucker model, which reduces memory, bandwidth, and energy consumption while maintaining data quality, evaluated through experiments on real and simulated datasets.

Contribution

Introduces tenSVD, an efficient tensor compression method based on the Tucker model, with implementation and evaluation in R for high-dimensional data management.

Findings

Significant reduction in memory and bandwidth usage.

Comparable or improved data quality preservation.

Lower energy consumption compared to baseline algorithms.

Abstract

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.