Image Compression: A Survey <s> Remote sensing images <s> We develop a general algorithm for decomposition and compression of grayscale images. The decomposition can be expressed as a functional relation between the original image and the Hadamard waveforms. The dynamic adaptive clustering procedure incorporates potential functions as a similarity measure for clustering as well as a reclustering phase. The latter is a multi-iteration, convergent procedure which divides the inputs into nonoverlapping clusters. These two techniques allow us to efficiently store and transmit a class of half-tone medical images such as magnetic resonance imaging (MRI) of the human brain. Due to the redundant image structure of MRI, obtained after the decomposition and clustering, almost half of the image can be omitted all together. Naturally, the compression rates for this specific type of grayscale image are increased greatly. A run-length coding is performed in order to compress further the retained information from the first two steps. Although all the techniques applied are simple, they represent an efficient way to compress grayscale images. The algorithm exhibits a performance which is competitive and often outperforming some of the methods reported in the literature. <s> BIB001 </s> Image Compression: A Survey <s> Remote sensing images <s> A lossless compression algorithm for images based on ordered binary-decision diagrams (OBDDs) is presented. The algorithm finds an OBDD which represents the image exactly and then codes the OBDD efficiently. The results obtained show a great improvement with respect to a previous work. <s> BIB002 </s> Image Compression: A Survey <s> Remote sensing images <s> The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression. <s> BIB003
Achieve compression ratios of 10:1 compression of grayscale images. The AIW represents an approach for obtaining the inverse solution using a layered network with BP algorithm. It is able to utilize a pair of different data types, corresponding to different physical quantities BIB002 BIB001 . Fast fractal image compression using the hadamard transforms: The algorithm using the HT has been proposed to increase the speed of encoder and still good quality of images is provided for the FIC. The improvement is achieved by removing all computations which are repeated of the eight dihedral symmetries of domain blocks. In performing the HT only +,operations are included these operations are more suitable for software and hardware implementations. This new algorithm can be easily applied most of the fast algorithms such as quad tree and classification mechanisms BIB003 .