Information-theoretic analysis of steganalysis in real images
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency
Multidimensional Systems and Signal Processing
Embedded zerotree wavelets coding based on adaptive fuzzy clustering for image compression
Image and Vision Computing
Improved adaptive wavelet threshold for image denoising
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Adaptive wavelet threshold for image denoising by exploiting inter-scale dependency
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Quality-driven wavelet based PCG signal coding for wireless cardiac patient monitoring
Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief
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Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself “on the fly” to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature