## 1 Introduction  
Images serve as significant representational entities, functioning as versatile information carriers in various forms, including television, satellite imagery, medical imaging, and computer-stored visuals [1]. Digital images, generated by sampling and digitizing a 2D light intensity signal, inherently contain large volumes of data. This often poses significant challenges in terms of storage and transmission. Image compression stands as a fundamental technique aimed at addressing this challenge by minimizing the quantity of data required to represent an image, thereby optimizing storage and transmission efficiency. Built upon a strong foundation in the domains of signal and image processing, image compression has gained heightened importance, particularly in addressing bandwidth and memory constraints with the rapid expansion of digital image data [2, 3]. As a result,a variety of advanced image compression techniques have been developed to enhance storage capacity and streamline bandwidth utilization during transmission [4]. There is a crucial aspect to highlight at this stage. The compression algorithms to be utilized may vary depending on whether the purpose is image storage or transmission. When the goal is image transmission, real-time processing constraints become a critical consideration. Conversely, when the goal is image storage, often as part of an archiving process, there are generally no significant restrictions on the compression algorithms that can be applied. The fundamental difference between these two approaches lies in the fact that during the storage process, algorithms are not required to operate in real time. As a result, there is no need to buffer the data obtained at the end of the encoding process to match the transmission speed of the communication channel [5]. The compression algorithm applied to an image varies not only based on storage or transmission purposes but also according to the structure of the image and its intended use. To meet these objectives, image compression techniques are broadly classified in the literature into two categories: lossless and lossy compression [6; 7]. In lossless image compression methods, the compressed image must be an exact copy of the original image. Lossless compression algorithms are employed in scenarios where any loss or alteration of data is unacceptable. Notable examples include medical imaging [8; 9], thermal imagery acquired through nano-satellite technology [10], and images used for critical applications such as remote sensing [11]. Lossy compression algorithms, a commonly employed alternative, allow for the intentional loss of certain information from the original image, with the primary goal of achieving significantly higher compression ratios. Images compressed using this method exhibit some degree of distortion compared to the original image. However, the success and efficiency of lossy compression algorithms are assessed based on the extent of data distortion, the achieved compression ratio, and the computational complexity of the algorithm [6; 12; 13]. Lossy compression algorithms are often preferred due to their ability to significantly reduce file sizes. This reduction is achieved by discarding certain image data deemed less critical to human visual perception. During this process, techniques such as approximation and quantization are applied to the image. However, the inherent information loss resulting from the algorithm's design can pose challenges in applications such as pattern recognition or optical character recognition processes [14]. Lossy compression algorithms are commonly utilized for transmitting images over the web [15], processing data from IoT sensors [16] and EEG signals [17]. A comprehensive understanding of image compression requires an in-depth exploration of how digital images are represented within the field of computer science.