利用遗传算法引导的关键像素选择实现图像存储记忆效率的紧凑表示法

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-04 DOI:10.1016/j.engappai.2024.109540
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引用次数: 0

摘要

在过去几年中,我们注意到数字内容的快速增长。即使在生物领域,用于生物研究的微观和纳米图像及视频的出现也增加了对存储空间的需求。因此,以存储效率高的方式存储这些数据已成为当务之急。在这项工作中,我们引入了一种紧凑型图像表示技术,该技术着眼于保留图像的形状,可以减少存储所需的内存。紧凑图像表示法不同于图像压缩,因为它不包括任何编码机制。相反,这种机制的理念是存储关键像素的位置,在需要时,可以重新生成原始图像。遗传算法用于选择关键像素,而高斯核则借助所选关键像素的位置执行重建任务。该模型在四个不同的数据集上进行了测试。在使用比特减少率进行评估时,建议的技术将内存需求减少了 87% 至 98%。然而,在使用结构相似性指数(范围在 0.81 到 0.94 之间)或均方根误差(范围在 0.06 到 0.08 之间)等指标进行评估时,重建图像的质量有点低。为了研究重构图像质量下降在实际应用中的影响,我们使用重构样本进行了图像分类,发现与使用原始样本进行分类相比,分类准确率下降了 0.13% 到 2.30%。建议模型的性能可与最先进的类似解决方案相媲美。
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Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection
In the past few years, we have observed rapid growth in digital content. Even in the biological domain, the arrival of microscopic and nanoscopic images and videos captured for biological investigations increases the need for space to store them. Hence, storing these data in a storage-efficient manner is a pressing need. In this work, we have introduced a compact image representation technique with an eye on preserving the shape that can shrink the memory requirement to store. The compact image representation is different from image compression since it does not include any encoding mechanism. Rather, the idea is that this mechanism stores the positions of key pixels, and when required, the original image can be regenerated. The genetic algorithm is used to select key pixels, while the Gaussian kernel performs the reconstruction task with the help of the positions of the selected key pixels. The model is tested on four different datasets. The proposed technique shrinks the memory requirement by 87% to 98% while evaluated using the bit reduction rate. However, the reconstructed images’ quality is a bit low when evaluated using metrics like structural similarity index (ranges between 0.81 to 0.94), or root means squared error (ranges between 0.06 to 0.08). To investigate the impact of quality reduction in reconstructed images in real-life applications, we performed image classification using reconstructed samples and found 0.13% to 2.30% classification accuracy reduction compared to when classification is done using original samples. The proposed model’s performance is comparable to state-of-the-art’s similar solutions.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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