Abstract
Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.
Highlights
Compression only minimizes the needed number of bits in order symbolize a video file or an image without dramatically affecting input quality
This section consists of the history of neural network techniques, mainly the Multilayer Perceptron Network (MLP), the Random Neural Network, the Recurrent Networks (RNN) and the Convolutional Neural Network (CNN)
Neural network is a recent compression tool because data processing is in parallel manner and requires less time, and it is general performance is superior to any other technique
Summary
Compression only minimizes the needed number of bits in order symbolize a video file or an image without dramatically affecting (original) input quality. The main aim of compression of the image is to reduce the amount of data required to symbolize an image. The compression of images reduces the redundancies and irrelevances in the data. In contrast with the original image there are fewer bits needed to symbolize a compressed image. There are fewer bits needed to symbolize a compressed image in comparison to the original picture. Three fundamental redundancies are common: 1.1 Coding redundancy The same number of bits is used in the normal image for more likely symbols and for lower probability symbols. Redundancy between pixels of statistical dependence, between adjacent pixels [6]
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