|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM171233948 |
| 003 |
DE-627 |
| 005 |
20250208105826.0 |
| 007 |
tu |
| 008 |
231223s2007 xx ||||| 00| ||eng c |
| 028 |
5 |
2 |
|a pubmed25n0571.xml
|
| 035 |
|
|
|a (DE-627)NLM171233948
|
| 035 |
|
|
|a (NLM)17605375
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Velisavljević, Vladan
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Space-frequency quantization for image compression with directionlets
|
| 264 |
|
1 |
|c 2007
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
|
| 338 |
|
|
|a Band
|b nc
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 26.07.2007
|
| 500 |
|
|
|a Date Revised 26.10.2019
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Beferull-Lozano, Baltasar
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Vetterli, Martin
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 16(2007), 7 vom: 28. Juli, Seite 1761-73
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
|
| 773 |
1 |
8 |
|g volume:16
|g year:2007
|g number:7
|g day:28
|g month:07
|g pages:1761-73
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 16
|j 2007
|e 7
|b 28
|c 07
|h 1761-73
|