Smart wavelet image coding: X-tree approach
Signal Processing
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
What's Your Sign?: Efficient Sign Coding for Embedded Wavelet Image Coding
DCC '00 Proceedings of the Conference on Data Compression
Modifications of Uniform Quantization Applied in Wavelet Coder
DCC '00 Proceedings of the Conference on Data Compression
Trees, Windows, and Tiles for Wavelet Image Compression
DCC '00 Proceedings of the Conference on Data Compression
Feature preserving image compression
Pattern Recognition Letters
Science of Continuous Media Application Design in Wireless Networks of Mobile Devices
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
Embedded zerotree wavelets coding based on adaptive fuzzy clustering for image compression
Image and Vision Computing
Mutual information-based context quantization
Image Communication
Adaptive Compressed Image Sensing Using Dictionaries
SIAM Journal on Imaging Sciences
Detail Preserving Wavelet-based Compression with Adaptive Context-based Quantisation
Fundamenta Informaticae
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We present an adaptive image coding algorithm based on novel backward-adaptive quantization/classification techniques. We use a simple uniform scalar quantizer to quantize the image subbands. Our algorithm puts the coefficient into one of several classes depending on the values of neighboring previously quantized coefficients. These previously quantized coefficients form contexts which are used to characterize the subband data. To each context type corresponds a different probability model and thus each subband coefficient is compressed with an arithmetic coder having the appropriate model depending on that coefficient's neighborhood. We show how the context selection can be driven by rate-distortion criteria, by choosing the contexts in a way that the total distortion for a given bit rate is minimized. Moreover the probability models for each context are initialized/updated in a very efficient way so that practically no overhead information has to be sent to the decoder. Our results are comparable or in some cases better than the recent state of the art, with our algorithm being simpler than most of the published algorithms of comparable performance.