In the realm of video and image compression, the evolution and future prospects are explored in this post. Starting with the famous JPEG standard and its lossy codec, which strategically discards less important data, a deep dive into neural compression is provided. From autoencoders to learned image codecs, the content delves into various techniques such as variable rate control and perceptual loss functions. Breakthroughs in end-to-end training models optimizing rate-distortion trade-offs and using GAN discriminators have shown promise in surpassing traditional codecs. Despite computational challenges, a blend of neural and traditional approaches appears to be the key to efficient image and video compression in the future.
https://mlumiste.com/technical/compression-deep-learning/