Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Lossy Image CompressionImage Compression Using Quality Measures

Lossy Image Compression: Image Compression Using Quality Measures [This chapter discusses domain decomposition algorithms using quality measures like average difference, entropy, mean squared error and a fuzzy geometry measure called fuzzy compactness. All the partitioning methods discussed in this chapter execute in O(nlogn) time for encoding and θ(n) time for decoding, where n is the number of pixels in the image.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Lossy Image CompressionImage Compression Using Quality Measures

Download PDF
22 pages

Loading next page...
 
/lp/springer-journals/lossy-image-compression-image-compression-using-quality-measures-cM8Bkxyrcw
Publisher
Springer London
Copyright
© K.K. Shukla 2011
ISBN
978-1-4471-2217-3
Pages
43 –64
DOI
10.1007/978-1-4471-2218-0_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter discusses domain decomposition algorithms using quality measures like average difference, entropy, mean squared error and a fuzzy geometry measure called fuzzy compactness. All the partitioning methods discussed in this chapter execute in O(nlogn) time for encoding and θ(n) time for decoding, where n is the number of pixels in the image.]

Published: Aug 27, 2011

Keywords: Image quality; Average difference; Entropy; ME; Fuzzy sets; Fuzzy compactness

There are no references for this article.