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.
- Lossy compression
- Lossless compression
The proposed technique indicates that using 3D SPIHT combined with wavelets is a promising technique for II video compression. evaluated for different mother wavelet functions at different compression levels. SPIHT compression results show that the PSNR is 5 dB higher than 2D SPIHT for the used II images. At lower compression ratio the PSNR for 3D SPIHT is about 4 dB higher than 2D SPIHT. The image’s PSNR will be directly related to the amount of the file received from the transmitter. This means that the image quality will only increase with the percentage of the file received. After the SPIHT transformation some regularities will exist in the file. These regularities may allow us to further compress the file. (SPIHT) is a wavelet-based image compression coder that offers a variety of good characteristics. These characteristics include:
- Good image quality with a high PSNR
- Fast coding and decoding
- A fully progressive bit-stream
- Can be used for lossless compression
- Ability to code for exact bit rate or PSNR
The scheme takes advantage of the viewpoints image representation of unidirectional images to decrease the number of bits required to code the image. However, this achieves a reduction in the bit rate and a significant reduction in the computational cost
- Tracking Position
- Medical Imaging
Matlab GUI for 3D SPIHT
Conclusion In this work, we present and evaluate a compression scheme based on applying the 3D wavelet transform with SPIHT on the Integral Images. The results are compared to II compression using 2D SPIHT. The proposed system performance is evaluated in terms of both bit rate and the recovered image quality. PSNR is used to evaluate the quality of the recovered image. The performance is also
 G. Lippmann, “La photographie integrale,” C. R. Acad. Sci. 146, 446-451 (1908).
 T. Okoshi, Three-dimensional imaging techniques (Academic Press, New York, 1976).
 T. Okoshi, “Three-dimensional displays,” in Proceedings of IEEE 68, 548-564 (1980).
 H. Hoshino, F. Okano, H. Isono and I. Yuyama, “Analysis of resolution limitation of integral photography,” J.Opt. Soc. Am. A 15, 2059-2065 (1998)
 J.-H. Park, K. Hong, and B. Lee, “Recent progress in threedimensional information processing based on integral imaging,” Appl. Opt. 48(34), H77–H94 2009.
 R. Zaharia, A. Aggoun, and M. McCormick, “Adaptive 3DDCT compression algorithm for continuous parallax 3D integral imaging,” J. Signal Process.: Image Commun., vol. 17, no. 3, pp. 231–242, 2002.
 3D Imaging Technol. Group, Brunel Univ., Uxbridge, “A 3D Dct Compression Algorithm For Omnidirectional Integral Images,” 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2006.
 S. Yeom, A. Stern, and B. Javidi, “Compression of 3-D color integral images,” Opt. Express, vol. 12, pp. 1632– 1642, Apr. 2004.