Image Compression Techniques Based on Quantization and Statistical Coding

Due to technical advancements that do not impact the range of picture operations, the availability of powerful image alteration software, or image management, the availability of images in a broad variety of applications has grown. The need for more storage space and more bandwidth for communications still outstrips what is currently available, even if storage and transmission technologies have advanced significantly. Consequently, picture compression is a useful method. We prioritize reducing file size without compromising picture quality or information when we compress images. The study provides an overview of the most popular lossy and lossless image compression methods, as well as their advantages, disadvantages, and potential future research directions. By comparing several compression methods, we can better pick the one that best suits our needs by highlighting its best features. We provide some broad guidelines for selecting the best picture compression algorithm in light of the review's findings. In addition, a comprehensive tabular analysis comparing the approaches' efficacy in picture compression is provided to showcase the assessment and performance of this research.