Compression of Depper Images for Hybrid Contexts of Picture Order and Recreation

Abstract

Progressive deep image compression is a method for compressing digital images using deep learning techniques. It is an extension of traditional image compression methods, such as JPEG and PNG, which use a combination of mathematical algorithms to compress images. In progressive deep image compression, a deep neural network is trained to learn how to compress images in a way that preserves image quality while reducing file size. The network is trained in a progressive manner, where the compression quality is gradually increased as the network is trained on more data. The main advantage of progressive deep image compression is that it can achieve higher compression ratios while maintaining image quality compared to traditional methods. This is because the neural network can learn to identify and preserve the most important features of an image while discarding less important information. The use of deep learning in image compression is a rapidly evolving area of research, with many new techniques and algorithms being developed. Progressive deep image compression is one such technique that shows promise in improving the efficiency of image compression for a wide range of applications. Results show the superiority over the existing approaches and performance metrics supports the proposed model in progressive image compression.