  The application of machine learning(ML) and genetic programming(GP) to the
image compression domain has produced promising results in many cases. The need
for compression arises due to the exorbitant size of data shared on the
internet. Compression is required for text, videos, or images, which are used
almost everywhere on web be it news articles, social media posts, blogs,
educational platforms, medical domain, government services, and many other
websites, need packets for transmission and hence compression is necessary to
avoid overwhelming the network. This paper discusses some of the
implementations of image compression algorithms that use techniques such as
Artificial Neural Networks, Residual Learning, Fuzzy Neural Networks,
Convolutional Neural Nets, Deep Learning, Genetic Algorithms. The paper also
describes an implementation of Vector Quantization using GA to generate
codebook which is used for Lossy image compression. All these approaches prove
to be very contrasting to the standard approaches to processing images due to
the highly parallel and computationally extensive nature of machine learning
algorithms. Such non-linear abilities of ML and GP make it widely popular for
use in multiple domains. Traditional approaches are also combined with
artificially intelligent systems, leading to hybrid systems, to achieve better
results.
