This paper reviews recent advancements in image compression techniques, focusing on both classical and region-of-interest (ROI)-based approaches. Image compression is crucial for applications requiring efficient storage and transmission, such as automotive systems and telemedicine. The paper highlights the importance of preserving the quality of the region of interest while reducing the size of the non-interest areas. Classical compression methods, including lossless and lossy techniques, are discussed, along with their performance metrics such as compression ratio (CR), mean-square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The paper also examines hybrid methods that combine lossless and lossy compression to achieve better results in specific domains like medical imaging and automotive systems. The review includes detailed analyses of various ROI-selection methods, such as mathematical, masked-based, segmentation, interactive, growth, and neuronal network approaches. The paper concludes by comparing the performance of different compression techniques and providing insights into the best practices for selecting appropriate algorithms based on specific application requirements.This paper reviews recent advancements in image compression techniques, focusing on both classical and region-of-interest (ROI)-based approaches. Image compression is crucial for applications requiring efficient storage and transmission, such as automotive systems and telemedicine. The paper highlights the importance of preserving the quality of the region of interest while reducing the size of the non-interest areas. Classical compression methods, including lossless and lossy techniques, are discussed, along with their performance metrics such as compression ratio (CR), mean-square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The paper also examines hybrid methods that combine lossless and lossy compression to achieve better results in specific domains like medical imaging and automotive systems. The review includes detailed analyses of various ROI-selection methods, such as mathematical, masked-based, segmentation, interactive, growth, and neuronal network approaches. The paper concludes by comparing the performance of different compression techniques and providing insights into the best practices for selecting appropriate algorithms based on specific application requirements.