Vector quantization-driven image compression through multi-objective evolutionary algorithms - Camacho-Gonzalez, Francisco David; Lima-López, Daniel; Zapotecas-Martínez, Saúl; Altamirano-Robles, Leopoldo (Expert systems with applications, 2025)
In recent years, information consumption among the population has increased, especially through the widespread use of digital images and videos. Consequently, the demand for efficient compression methods has escalated, aiming to facilitate both data storage and transmission while adapting to the unique requirements of diverse problems. The image compression problem has been explored in several works through the lens of vector quantization (VQ) by adopting a single-objective approach to achieve optimal quality results but with a fixed compression level. In contrast, this investigation introduces a groundbreaking multi-objective compression approach that simultaneously optimizes image quality and compression level. This is achieved by selecting the most representative information to construct an adequate codebook while minimizing its size. The proposed method was evaluated on a curated selection of well-known public domain images, using different types of multi-objective evolutionary algorithms, including Pareto-, indicator- and decomposition-based approaches. The compressed images produced were assessed through the application of established quality indicators drawn from the expansive realm of image processing literature, ensuring the reliability and validity of our findings. The results demonstrate the proposed method’s ability to adapt to the intrinsic characteristics of each image, removing the maximum amount of redundant information presented. Furthermore, the multi-objective nature of the proposed approach allows us to obtain solutions with different levels of trade-off between quality and compression, opening up new possibilities in the field and demonstrating the potential to address the evolving challenges posed by the escalating volume of information in the digital landscape.