An improved multimodal signal-image compression scheme with application to natural images and biomedical data

Abstract

In this paper, a new multimodal compression scheme is proposed with the aim of compressing jointly an image and a signal via a single codec. The key idea behind our approach is to insert a wavelet-decomposed signal into a decomposed image and then consider the mixture data as an image for compression with the Set Partitioning In Hierarchical Trees (SPIHT) encoder. The insertion stage is performed in detail wavelet sub-bands using a spiral insertion function. The evaluation process is assessed on both natural and medical images according to an objective and subjective comparison criteria. Moreover, four multimodal compression schemes are provided for the sake of fair assessment. Finally, experimental results demonstrate the effectiveness of the proposed approach to achieve significant gains in terms of Percentage of Root Mean Square Difference (PRD) and Peak Signal to Noise Ratio (PSNR) for both reconstructed signal and image.