Matlab code for 3D SPIHT

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In three-dimensional display based on integral imaging (II) the compression of the elemental images is a major need to be implemented in real time applications. In this paper, we propose an Integral Imaging (II) lossless compression coder based on three-dimensional set partitioning in hierarchical trees,3D SPIHT. The elemental images are stacked to form a three dimensional image. ?wavelet transform is performed, then 3D SPIHT coding is applied. Simulations are performed to test the performance of the 3D compression system. The results show that the proposed system has superior compression Performance compared to 2 DSPIHT.

Image compression is important for many applications that involve huge data storage, transmission and retrieval such as for multimedia, documents, videoconferencing, and medical imaging. Uncompressed images require considerable storage capacity and transmission bandwidth. The objective of image compression technique is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form. This results in the reduction of file size and allows more images to be stored in a given amount of disk or memory space . Image compression can be lossy or lossless In a lossless compression algorithm, compressed data can be used to recreate an exact replica of the original; no information is lost to the compression process. This type of compression is also known as entropy coding. This name comes from the fact that a compressed signal is generally more random than the original; patterns are removed when a signal is compressed. While lossless compression is useful for exact reconstruction, it generally does not provide sufficiently high compression ratios to be truly useful in image compression.

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