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The application of machine learning(ML) and genetic programming(GP) to theimage compression domain has produced promising results in many cases. The needfor compression arises due to the exorbitant size of data shared on theinternet. Compression is required for text, videos, or images, which are usedalmost everywhere on web be it news articles, social media posts, blogs,educational platforms, medical domain, government services, and many otherwebsites, need packets for transmission and hence compression is necessary toavoid overwhelming the network. This paper discusses some of theimplementations of image compression algorithms that use techniques such asArtificial Neural Networks, Residual Learning, Fuzzy Neural Networks,Convolutional Neural Nets, Deep Learning, Genetic Algorithms. The paper alsodescribes an implementation of Vector Quantization using GA to generatecodebook which is used for Lossy image compression. All these approaches proveto be very contrasting to the standard approaches to processing images due tothe highly parallel and computationally extensive nature of machine learningalgorithms. Such non-linear abilities of ML and GP make it widely popular foruse in multiple domains. Traditional approaches are also combined withartificially intelligent systems, leading to hybrid systems, to achieve betterresults.

An Implementation of Vector Quantization using the Genetic Algorithm Approach