Unsupervised neural network algorithm for image compression

1992 ◽  
Author(s):  
Dejun Cai ◽  
Wei Wang ◽  
Faguang Wan
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Network Algorithm ◽  
Unsupervised Neural Network ◽  
Neural Network Algorithm

scholarly journals Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

2021 ◽  
pp. 47-57
Author(s):  
Diyar Waysi Naaman
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Back Propagation ◽  
Image Transmission ◽  
Search Space ◽  
Compression Technique ◽  
Advanced Technique ◽  
Fractal Image ◽  
Network Algorithm ◽  
Neural Network Algorithm

Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

Application of FA-LMBP Hybird Neural Network Algorithm in Image Compression

2019 ◽  
Vol 56 (19) ◽  
pp. 191005
Author(s):  
王海军 Wang Haijun ◽  
金涛 Jin Tao ◽  
门克内木乐 Men Ke Neimule
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Network Algorithm ◽  
Neural Network Algorithm

Effective Multiple Vector Quantization for Image Compression

2007 ◽  
Vol 11 (10) ◽  
pp. 1189-1196
Author(s):  
Noritaka Shigei ◽  
◽  
Hiromi Miyajima ◽  
Michiharu Maeda ◽  
Lixin Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Image Restoration ◽  
Vector Quantization ◽  
High Quality ◽  
Index Coding ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Compressed Data

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: “index inference” and “ranking based index coding.” It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm
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