The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user opinion in advance. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

翻译：JPEG标准广泛应用于各类图像处理应用中。量化表（QT）作为JPEG标准的核心组件之一，对图像质量与文件大小等图像特性起着决定性作用。近年来，研究人员基于种群元启发式（PBMH）算法开展了多项探索，旨在为特定图像寻找最优量化表，但这些研究未能预先考虑用户偏好。以安卓开发者为例，当其倾向于生成小尺寸图像时，优化过程却可能输出高质量图像，导致文件体积过大。现有研究的另一缺陷在于覆盖面不足，即生成的量化表无法涵盖文件大小与质量的所有可能组合。因此，本文提出三项创新性贡献：第一，将用户偏好纳入压缩流程，用户可预先控制输出图像的文件大小。为此，我们设计了面向种群JPEG图像压缩的新型目标函数；第二，针对覆盖面不足的问题，提出全新表征方法。该表征不仅能实现更全面的覆盖，还能在无需背景知识的前提下为特定图像确定最优质量因子参数。表征方法与目标函数的改进均独立于搜索策略，可兼容任意类型的种群元启发式算法。作为第三项贡献，我们对22种最新提出的先进PBMH算法开展了系统基准测试。基于不同基准图像的多维度实验表明，本文提出的JPEG图像压缩新框架具有显著有效性。