Image compression is nothing but reducing the amount of data required to represent an image. To compress an image efficiently we use various techniques to decrease the space and to increase the efficiency of transfer of the images over network for better access. This paper explains about compression methods such as JPEG 2000, EZW, SPIHT (Set Partition in Hierarchical Trees) and HS-SPIHT on the basis of processing time, error comparison, mean square error, peak signal to noise ratio and compression ratio. Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in many conditions especially in the memory constraint case. SPIHT gives better simplicity and better compression compared to the other techniques. But to scale the image more so as to get better compression we are using the line-based Wavelet transform because it requires lower memory without affecting the result of Wavelet transform. We proposed a highly scalable image compression scheme based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. This algorithm is called Highly Scalable SPIHT (HS_SPIHT) it gives good scalability and provides 1 bit stream that can be easily adapted to give bandwidth and resolution requirements.
CITATION STYLE
. Y. S. (2013). ANALYSIS OF IMAGE COMPRESSION ALGORITHMS USING WAVLET TRANSFORM WITH GUI IN MATLAB. International Journal of Research in Engineering and Technology, 02(10), 595–603. https://doi.org/10.15623/ijret.2013.0210093
Mendeley helps you to discover research relevant for your work.