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.