Image compression with MLPs involves a unitary transformation of the entire spatial data. Theoretically, MLPs with multiple hidden layers are useful for dimension reduction and data compression. Multi-layer perceptrons (MLPs) have a hidden layer (or layers) or neurons sandwiched between a layer of input neurons and a layer of output neurons. For example, records maintained by security and government agencies require standardized images.ĭeep Learning (DL) has been used for image compression since the 1980s, and has evolved to include techniques such as multi-layer perceptrons, random neural networks, convolutional neural networks and generative adversarial networks. Standardization - in some cases, sets of images must conform to a standard size and format, requiring compression of all images into the same size, shape and resolution.

Principal component analysis (PCA) - image compression methods that extract the most significant components of an image are used for extracting or summarizing features and analyzing data.

Storage - the compressed data takes up less disk space, which is particularly useful for archiving detailed images, such as medical images.

Image compression is a form of data compression, as it reduces the data bits required to encode images but preserves image details.Īpplications of image compression include:

Advanced image optimization techniques can identify the more important image components and discard less crucial components. This reduces their storage and processing burden (for web performance). Image compression involves reducing the pixels, dimensions or color components of images in order to reduce their file size. Image Source: Pixabay What Is Image Compression?

Image compression software open source