论文标题
能源感知的JPEG图像压缩：一种多目标方法
Energy-Aware JPEG Image Compression: A Multi-Objective Approach
论文作者
论文摘要
客户满意度在移动设备中的能源消耗至关重要。应用程序中最耗能的部分之一是图像。尽管具有不同质量的不同图像消耗了不同量的能量，但没有直接的方法来计算典型图像中操作的能量消耗。首先，本文调查了能源消耗与图像质量以及图像文件大小之间存在相关性。因此，这两个可以被视为能源消耗的代理。然后，我们提出了一种多目标策略，以增强图像质量并根据JPEG图像压缩中的定量表减少图像文件大小。为此，我们使用了两种一般的多目标元启发式方法：标量和基于帕累托。标量方法找到基于组合不同目标的单个最佳解决方案，而基于帕累托的技术旨在实现一组解决方案。 In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search （enmops）。此外，使用两种基于帕累托的方法，包括非主导的排序遗传算法（NSGA-II）和基于参考点的NSGA-II（NSGA-III）进行嵌入方案，以及两个基于帕托的算法，ENNSGAII和ENNSGAIII。实验研究表明，通过将提出的策略嵌入元启发式算法中，可以改善基线算法的性能。
Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. While different images with different quality consume different amounts of energy, there are no straightforward methods to calculate the energy consumption of an operation in a typical image. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. Then, we propose a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation tables in JPEG image compression. To this end, we have used two general multi-objective metaheuristic approaches: scalarisation and Pareto-based. Scalarisation methods find a single optimal solution based on combining different objectives, while Pareto-based techniques aim to achieve a set of solutions. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms.
