Key result
Single-codec multimodal compression matches separate encoding quality at bitrates above ~0.50 bpp.
A novel multimodal compression technique improves compression ratios for combined medical image and biosignal data compared to separate encoding.
May streamline joint image-biosignal storage; hypothesis-generating and requires clinical validation.
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.
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Zeybek et al. (2012) studied this question. Multimodal Compression (MC) vs. Separate compression of modalities was evaluated on Reconstruction quality (PSNR for image, PRD for signal). Multimodal compression of medical images and biosignals using a single JPEG 2000 codec achieved comparable reconstruction quality to separate compression at bitrates above 0.50 bpp.
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