logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Improved class-based coding of multispectral image's with shape-adaptive wavelet transform

delete2007-10-01
delete19
PRE
AI
M
Marco Cagnazzo *
S
Sara Parrilli
G
Giovanni Poggi
L
Luisa Verdoliva
DOI：10.1109/LGRS.2007.900696delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this letter, we improve the class-based transformcoding scheme proposed by Gelli and Poggi for the compression of multispectral images. The original spatial-coding tools, 1-D discrete cosine transform and scalar quantization, are replaced by shape-adaptive wavelet transform and set partitioning in hierarchical trees. Numerical experiments show that the improved technique outperforms the original one for medium- to highquality compression and is consistently superior to all reference techniques.
Keywords:
compression
multispectral
segmentation
shape-adaptive wavelet transform (SAWT)
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Geoscience and Remote Sensing Magazine cover
IEEE Geoscience and Remote Sensing Magazine
IF:
16.4
Papers:
1.0W
Citations:
5.1K

Organization

No organization information available