Image compression is a technique used to reduce the file size of an image while maintaining an acceptable level of image quality. It works by eliminating or reducing the redundancy and irrelevant information in an image.

  There are two main types of image compression: lossless compression and lossy compression.

  1. Lossless Compression:

  Lossless compression algorithms reduce the file size without losing any image data. These algorithms typically exploit redundancy in the image data by finding patterns or repeated sequences. The most commonly used lossless compression algorithm for images is the DEFLATE algorithm, which is used in the popular ZIP file format. Other algorithms include LZW (used in GIF files) and PNG.

  2. Lossy Compression:

  Lossy compression algorithms reduce the file size by discarding some of the image data that are deemed less important or imperceptible to the human eye. These algorithms take advantage of the limitations of human visual perception. They achieve high compression ratios by removing details that are less important or by approximating the color information. Lossy compression is commonly used for images on the web and in multimedia applications. The JPEG (Joint Photographic Experts Group) compression algorithm is the most widely used lossy compression technique for images.

  Lossy compression techniques often use a combination of several methods:

  a. Quantization: This process reduces the number of colors in an image by grouping similar colors together. It is done by dividing the color range into smaller intervals and mapping the colors to these intervals.

  b. Discrete Cosine Transform (DCT): DCT is a mathematical transformation that converts the spatial image data into frequency domain data. By applying DCT, the image data can be represented by a set of coefficients that represent various frequency components. These coefficients can be quantized and compressed more efficiently.

  c. Huffman Coding: This technique is used to compress the quantized coefficients further by assigning shorter codes to frequently occurring coefficients and longer codes to rarely occurring coefficients.

  d. Chroma Subsampling: This technique reduces the resolution of the color information (chroma) compared to the brightness information (luma) in an image, as human eyes are less sensitive to color differences compared to brightness.

  It is worth noting that each compression algorithm has its own advantages and limitations, and the choice of compression method depends on the specific requirements of the image and the application in which it will be used.