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Spectral Information Divergence (SID) was identified as the most efficient spectral similarity measure. However, we show that divergence are not adapted to direct use on spectra. Following an idea proposed by Nidamanuri, we construct a spectral pseudo-divergence based on the Kullback-Leibler divergence. This pseudo-divergence is composed of two parts: a shape and an intensity similarity measure. Consequently, bidimensional representation of spectral differences are constructed to display the histograms of similarity between a spectral reference and the spectra from a data-set or an hyperspectral image. We prove the efficiency of the spectral similarity measure and of the bidimensional histogram of spectral differences on artificial and Cultural Heritage spectral images.

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In this paper, we introduce a novel approach to compress jointly a Multi-Channel Electrocardiogram (MCE) and an ultrasound image. We will show that this technique allows better performances, in terms of compression ratio (CR) compared to coding separately both modalities. In this approach, scaled ECG samples are inserted within the high frequencies of the ultrasound image after its decomposition on wavelet basis. The new standard JPEG2000 is then applied on the packed data for both coding and decoding purpose. Finally, the reconstruction quality is evaluated using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for both the ultrasound image and the ECG signals.

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