DocumentCode :
2855338
Title :
Error Detection in SPIHT Coded Images
Author :
Tian Lin ; Zhang Xinghui
Author_Institution :
Dept. of Comput., Tianjin Univ. of Technol. & Educ. (TUTE), Tianjin, China
fYear :
2009
fDate :
19-20 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
In this paper we propose a novel approach based on slice of information detection in order to improve the error detection efficiency and accuracy of the set partitioning in hierarchical trees (SPIHT) algorithm. If decoder detects error data more (or less) than the block number which is set in the slice head, it breaks and marks that error. After the whole image is decoded, the decoder starts to detect correlation according to inner-block correlation (IC) and interact-block correlation (ICB). These detection thresholds (IC & ICB) are updated by measuring the mean average over the error block´s neighbor to locate the erroneous block. The erroneous block (whose parameter (IC or ICB) is greater than the threshold) is dealt with error concealment according to error concealment neighbor way. The simulation results show that the algorithm is simple, and can effectively find the errors. Comparing with the method without error detection and concealment, the decoder PSNR of reconstructed image has improved 0.3-1.1 dB.
Keywords :
correlation methods; decoding; error detection; image coding; image reconstruction; PSNR decoder; SPIHT coded images; correlation detection; error concealment; error data detection decoder; error detection efficiency; image reconstruction; information detection; inner-block correlation; interact-block correlation; peak signal to noise ratio; set partitioning in hierarchical tree algorithm; Computer errors; Computer science education; Decoding; Educational technology; Error correction; Image coding; Image reconstruction; Sorting; Streaming media; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
Type :
conf
DOI :
10.1109/ICIECS.2009.5365660
Filename :
5365660
Link To Document :
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