Block truncation coding using neural network-based vector quantization for image compression

2002 ◽  
Author(s):  
C. Angelakis ◽  
G.A. Maragakis ◽  
P. Stavroulakis
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Vector Quantization ◽  
Block Truncation Coding

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 NEURO-WAVELET MODEL USING FUZZY VECTOR QUANTIZATION FOR EFFICIENT IMAGE COMPRESSION

2009 ◽  
Vol 09 (02) ◽  
pp. 299-320
Author(s):  
VIPULA SINGH ◽  
NAVIN RAJPAL ◽  
K. SRIKANTA MURTHY
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Vector Quantization ◽  
High Efficiency ◽  
Pulse Code Modulation ◽  
Frequency Bands ◽  
Differential Pulse Code Modulation ◽  
Hidden Layer ◽  
Fuzzy Vector Quantization ◽  
Fuzzy Vector

Images have large data quantity. For storage and transmission of images, high efficiency image compression methods are under wide attention. In this paper, we propose a neuro- wavelet based model for image compression, which combines the advantages of wavelet transform and neural network and uses fuzzy vector quantization on hidden layer coefficients. Images are decomposed using wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. The coefficients in the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. The coefficients of the hidden layer of the neural network are further fuzzy vector quantized, which increases the compression ratio. The visual quality of the image has been increased by introducing fuzziness to vector quantization algorithm. Satisfactory reconstructed images with large compression ratios have been achieved using this scheme.

Image Compression Using Vector Quantization with Variable Block Size Division

2010 ◽  
Vol 130 (8) ◽  
pp. 1431-1439 ◽  
Author(s):  
Hiroki Matsumoto ◽  
Fumito Kichikawa ◽  
Kazuya Sasazaki ◽  
Junji Maeda ◽  
Yukinori Suzuki
Keyword(s):  
Image Compression ◽  
Vector Quantization ◽  
Block Size ◽  
Variable Block
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