scholarly journals MODIFICATION TO SPIHT ALGORITHM USING INCREMENTAL THRESHOLD FOR IMAGE COMPRESSION

Author(s):  
A. S. JADHAV ◽  
RASHMI V. PAWAR

The Modified SPIHT represents a more efficient implementation of the SPIHT algorithm by using variable thresholds to sort the list of insignificant pixels (LIP) and the list of insignificant sets (LIS). We observe two interesting facts: (I) most of the initial subsets in LIS are not only insignificant with respect to the maximum threshold, but also insignificant with respect to the smaller threshold. And (2) Most of the pixels generating from sorting LIS are smaller than the current threshold. Based on these two observations, it represents a new image codec method, which can make the binary encoded outputs more efficient, and can work well on different image sizes and different decomposition levels.

2011 ◽  
Vol 16 (7) ◽  
pp. 34-42 ◽  
Author(s):  
Mahesh Jayakar ◽  
K.V.S Anand Babu ◽  
Srinivas K

Author(s):  
Fangzhou He

<span lang="EN-US">To prolong the life cycle of wireless sensor network, the basic theory of wavelet transform and its application in image compression are described, and several classic image compression algorithms based on wavelet transform are studied in depth. A compression algorithm combining the improved wavelet transform and <a name="_Hlk527539123"></a>Set Partitioning in Hierarchical Trees (SPIHT) algorithm of hierarchical wavelet tree set segmentation is proposed to effectively balance the energy consumption of each node in the sensor network and prolong the life of the whole wireless sensor network and an improved distributed image compression and transmission algorithm is proposed based on the distributed multi-node cooperative processing algorithm based on wavelet transformation, and detailed analytical test and energy consumption simulation experiment are carried out to verify the feasibility of the algorithm. The results show that the platform effectively implements and verifies the proposed algorithm, which can effectively realize the compression and transmission of distributed images, equalize the energy consumption of each sensor node in the network, and has strong practicability.</span>


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