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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In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel biosignals (e.g. ECG, EEG). This technique is based on the idea of Multimodal Compression (MC) which requires only one codec instead of multiple codecs. Objectively, biosignal samples are merged in the spatial domain of the image using a specific mixing function. Afterwards, the whole mixture is compressed using JPEG 2000. The spatial mixing function inserts samples in low-frequency regions, defined using a set of operations, including down-sampling, interpolation, and quad-tree decomposition. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. The reconstruction quality is evaluated on a set of test data using the PSNR (Peak Signal Noise Ratio) and the PRD (Percent Root Mean Square Difference), respectively for the image and biosignals.