A Study of Image and Video Compression Using Neural Networks

  • Ruhiat Sultana, Javeed Mohammad, Nisar Ahmed


Picture and video coding developments have progressed by leaps and bounds in recent years. However, as image and video acquisition devices become more prevalent, the growth rate of image and video data is outpacing the compression ratio increase. It's been widely acknowledged that seeking more coding performance enhancement within the conventional hybrid coding paradigm is becoming increasingly difficult. Deep convolution neural network (CNN) is a form of neural network that has seen a resurgence in recent years and has seen a lot of success in the fields of artificial intelligence and signal processing. also offers a novel and promising image and video compression solution. We present a systematic, thorough, and up-to-date analysis of neural network-based image and video compression techniques in this paper. For images and video, the evolution and advancement of neural network-based compression methodologies are discussed. More precisely, cutting-edge video coding techniques based on deep learning and the HEVC system are introduced and addressed, with the goal of significantly improving state-of-the-art video coding efficiency. In addition, end-to-end image and video coding frameworks based on neural networks are examined, revealing intriguing explorations on next-generation image and video coding frameworks. The most important research works on image and video coding related topics using neural networks are highlighted, as well as future developments. The joint compression of semantic and visual information, in particular, is tentatively investigated in order to formulate high-efficiency signal representation structures for both human and machine vision. In the age of artificial intelligence, which are the two most popular signal receptors.