Author: Wui Pin LeePublisher:
ISBN:
Category : Image compression
Languages : en
Pages : 226
Book Description
Image Compression Using Neural Network Based Vector Quantization
Author: Wui Pin LeePublisher:
ISBN:
Category : Image compression
Languages : en
Pages : 226
Book Description
Color Image Compression Using Vector Quantization and Neural Networks
Author: Julio A. HernándezPublisher:
ISBN:
Category : Image compression
Languages : en
Pages : 126
Book Description
Image Compression by Vector Quantization of DCT Coefficients Using a Self-organzing Neural Network
Author: Kirankumar BoyapatiPublisher:
ISBN:
Category : Image compression
Languages : en
Pages : 140
Book Description
Adaptive Image and Video Compression Using Vector Quantization and Self-organizing Neural Networks
Author: Hui LiuPublisher:
ISBN:
Category : Image compression
Languages : en
Pages : 418
Book Description
Image Compression Using Cascaded Neural Networks
Author: Chigozie ObiegbuPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixed-architecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented.
Adaptive Vector Quantization by a Novel Adaptive Resonance Theory (ART)-based Neural Network for Image Compression
Author: Xuequin LiPublisher:
ISBN:
Category : Adaptive signal processing
Languages : en
Pages : 216
Book Description
Data Compression Using Artificial Neural Networks
Author: Bruce E. WatkinsDevelopment of Image Compression Algorithms
Author: Vipula SinghPublisher: LAP Lambert Academic Publishing
ISBN: 9783845436319
Category :
Languages : en
Pages : 172
Book Description
Applications, which need to store large database and/or transmit digital images requiring high bit-rates over channels with limited bandwidth, have demanded improved image compression techniques.. Encoding an image into fewer bits is useful in reducing the storage requirements in image archival systems, or in decreasing the bandwidth for image transmission. In standard image compression methods (e.g. JPEG), as the bits per pixel reduces, the picture quality deteriorates because of the use of bigger quantization step size. In this work, image compression techniques are designed keeping in mind the human visual system. Practical and effective image compression system based on Neuro-Wavelet models have been proposed which combines the advantages of neural network and wavelet transform with vector quantization. Fuzzy c-means and Fuzzy vector quantization algorithms have also been used to make use of uncertainty for the benefit of the clustering process. We have compared the performances of different clustering algorithms applied to the proposed encoder. Experimental results on real images of varying complexity have established the robustness and effectiveness of the method
Novel Image Compression Methods Based on Vector Quantization
Author: M. Mary Shanthi RaniPublisher: LAP Lambert Academic Publishing
ISBN: 9783659662737
Category :
Languages : en
Pages : 188
Book Description
The rapid growth and development of electronic imaging in the recent years has led to large scale digital media archives. These are increasingly becoming popular as more and more digital media contents are created and deployed online every day. A critical issue in designing such archives is effective storage of the data. Uncompressed data requires more storage and huge bandwidth for transmission. Though the cost of storage is rapidly dropping, compression still remains as a challenging issue due to the growing number of multimedia based online applications. This necessitates the design of highly efficient image compression systems which promise good image quality and compression ratios with low computational complexity. This book is an outcome of the research in vector quantization based methods for compressing still images. It discusses novel image compression methods with performance analysis using standard compression metrics and their vital role in real-time applications. The proposed methods can be used in applications like Medical Image Processing, Mobile Applications, Biometrics, Remote Sensing and other online web applications.
Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014
Author: Suresh Chandra SatapathyPublisher: Springer
ISBN: 3319119338
Category : Technology & Engineering
Languages : en
Pages : 838
Book Description
This volume contains 95 papers presented at FICTA 2014: Third International Conference on Frontiers in Intelligent Computing: Theory and Applications. The conference was held during 14-15, November, 2014 at Bhubaneswar, Odisha, India. This volume contains papers mainly focused on Data Warehousing and Mining, Machine Learning, Mobile and Ubiquitous Computing, AI, E-commerce & Distributed Computing and Soft Computing, Evolutionary Computing, Bio-inspired Computing and its Applications.