Matlab Code for Image Compression using SPIHT Algorithm-Image Processing Projects


Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level.

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Image compression is one of the important applications in data compression on its image. Image data requires huge amount of disk space and large bandwidths for transmission. Hence, image compression is necessary to reduce the amount of data required to represent digital image .Discrete Wavelet Transform (DWT) based image compression has been paid much attention in the past decades. DWT has been adopted as a new technical standard for still image compression. Set Partitioning in Hierarchical Trees (SPIHT) is the DWT-based image compression algorithm which is more powerful, efficient and more popular, due to the properties of fast computation, low memory requirement. Discrete wavelet transform (DWT) based Set Partitioning in Hierarchical Trees (SPIHT) algorithm is widely used in many image compression systems.



In recent years there has been an astronomical increase in the usage of computers for a variety of tasks. With the advent of digital cameras, one of the most common uses has been the storage, manipulation, and transfer of digital images. The files that comprise these images, however, can be quite large and can quickly take up precious memory space on the computer’s hard drive. In multimedia application, most of the images are in color. And color images contain lot of data redundancy and require a large amount of storage space. In this work, we are presenting the performance of different wavelets using SPIHT algorithm for compressing color image. In this R, G and B component of color image are converted to YCbCr before wavelet transform is applied. Y is luminance component; Cb and Cr are chrominance components of the image. Lena color image is taken for analysis purpose. Image is compressed for different bits per pixel by changing level of wavelet decomposition. Matlab software is used for simulation. Results are analyzed using PSNR and HVS property. Graphs are plotted to show the variation of PSNR for different bits per pixel and level of wavelet decomposition. 


  • DCT(Discrete cosine transform)
  • Huffman coding


  • Inefficient compression ratio
  • Accuracy is less
  • Visual quality is low
  • Security is less 


We propose a simple and effective method combined with Huffman encode for further compression. A large number of experimental results are shown that this method saves a lot of bits in transmission, further enhanced the compression performance 




  • Efficient compression ratio
  • Accuracy is high
  • Visual quality is high
  • Security is high


  • Remote sensing
  • Medical and Surveillance systems
  • Multimedia Security
  • Wireless Transmission system


The proposed lossy image compression algorithm is simple and effective method for grayscale image compression and is combined with Huffman encoding for further compression in this paper that saves a lot of bits in the image data transmission. There are very wide range of practical value for today that have a have number of image data is to be transmitted. 


[1] Ahmed,N.; Natarajan,T.; Rao,K.R.“ Discrete Cosine Transform,” IEEE Trans. on Computers, vol. C-32, pp. 90-93, Jan. 1974.

[2] Liu,C.P.; Poularikas,A.D.” A New Subband Coding Technique Using (JPEG) DCT for Image Compression,” IEEE Trans. on Image processing, pp.317-321,1996.

[3] Antonini,M.; Barlaud,M.; Mathieu, P.; Daubechies,I.“ Image Coding Using Wavelet Transform, ” IEEE Trans. on Image Processing, Vol. 1, No. 2, pp.205-220.1992.

[4] Ronald A.DeVore; Bjorn Jawerth; Bradley J. Lucier ” Image Compression Through Wavelet Transform Coding, ” IEEE Trans. on Information Theory, Vol.38.NO.2,pp.719- 746, MARCH 1992.

[5] Lewis,A.S.; Knowles,G. ” Image Compression Using the 2-D Wavelet Transform, ” IEEE Trans. on Image Processing, Vol. I . NO. 2, PP. 244 – 250, APRIL 1992.

[6] Shapiro,J.M. “ Embedded image coding using zerotree of wavelets coefficients,” IEEE Trans. on Signal Processing,pp.3445-3462, 1993

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