Proceeding of Southwest Symposium on Image Analysis and Interpretation
DOI: 10.1109/iai.1996.493741
|View full text |Cite
|
Sign up to set email alerts
|

Efficient image coding using multiresolution wavelet transform and vector quantization

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
2
0

Publication Types

Select...
3
2

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 16 publications
0
2
0
Order By: Relevance
“…Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005;Premaraju, 1996).…”
mentioning
confidence: 82%
“…Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005;Premaraju, 1996).…”
mentioning
confidence: 82%
“…Image compression algorithms based on discrete wavelet transform (DWT) [1], such as embedded zero wavelet (EZW) [2] and set partitioning in hierarchical trees(SPIHT) [3] provide excellent rate distortion performance with low encoding complexity. However, it is quite fragile against bit errors in wireless channels because a single bit error within SPIHT streams may cause misinterpretation of the later bits, and then leads to error propagation and possible loss of synchronization.…”
Section: Introductionmentioning
confidence: 99%