A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete cosine transform: algorithms, advantages, applications
Discrete cosine transform: algorithms, advantages, applications
An introduction to computing with neural nets
Artificial neural networks: theoretical concepts
Vector quantization and signal compression
Vector quantization and signal compression
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Improving Wavelet Compression with Neural Networks
DCC '01 Proceedings of the Data Compression Conference
Image compression using wavelet transform and multiresolution decomposition
IEEE Transactions on Image Processing
Comments on “modified K-means algorithm for vector quantizer design”
IEEE Transactions on Image Processing
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Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector quantization, wavelet based sub band decomposition and neural network. Vector quantization is often used when high compression ratios are required. The implementation consists of three steps. First, image is decomposed 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. At the second step, the wavelet coefficients corresponding to lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. The result of the second step is used as input to fuzzy vector quantizer. Image quality is compared objectively using mean squared error and PSNR along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality as compared to other image compression techniques.