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has been cited by the following article:

• TITLE: Multimodal compression applied to biomedical data

  AUTHORS: Emre H. Zeybek, Régis Fournier, Amine Naït-Ali

  KEYWORDS: Biomedical Signal Compression; Biomedical Image Compression; JPEG2000; Lossy Compression; Multimodal Compression; Quad-Tree Decomposition

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.5 No.12, December 26, 2012

  ABSTRACT: In this paper, we introduce a novel approach to compress jointly a medical image and a multichannel bio-signals (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.