The discrete modal transform and its application to lossy image compression
Image Communication
Conjugate symmetric sequency-ordered complex Hadamard transform
IEEE Transactions on Signal Processing
A new class of reciprocal-orthogonal parametric transforms
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A new transform for document image compression
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Dynamic range compression using Hadamard processing and decorrelation spreading
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Survey on the use of smart and adaptive engineering systems in medicine
Artificial Intelligence in Medicine
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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.