Paper Info
Title
Polynomial approximation and vector quantization: a region-based integration
Abstract
The paper presents an adaptive scheme for image-data compression. It is a region-based approach that suitably integrates two different approaches to image coding, vector quantization (VQ) and polynomial approximation (PA). The scheme is adaptive from the point of view of the human observer: the perceptually most significant areas are those near edges or details. In smoothed areas, PA can be used with notable results, but there VQ must be employed to ensure high fidelity. The two techniques exhibit a complementarity in both advantages and drawbacks. PA is not efficient in compressing high-frequency areas, but yields the best results when applied to highly correlated data. VQ is unable to reach high-compression ratios because of its low adaptability, but is quite suitable for compressing uncorrelated data. The means to achieve the integration of the two techniques is a control image containing information about edge and texture locations. In the paper, edge encoding and restoration are also addressed, which are closely related to the proposed hybrid scheme; block prediction is also utilized to further reduce the residual redundancy between VQ blocks. The exploitation of the best features of both approaches results in high compression factors, and in perceivable good quality. In particular, bitrates range from 0.15 to 0.07 bpp. Main applications of this compression scheme are in the areas of very-low bitrate image transmission and image archiving.
Year
DOI
Venue
1995
10.1109/26.380036
IEEE Transactions on Communications
Keywords
Field
DocType
adaptive codes,approximation theory,block codes,edge detection,image coding,image restoration,image segmentation,image texture,polynomials,prediction theory,vector quantisation,visual communication,adaptive scheme,applications,block prediction,control image,edge encoding,edge location,high fidelity,high-compression ratios,high-frequency areas,highly correlated data,human observer,image archiving,image coding,image restoration,image transmission,image-data compression,polynomial approximation,region-based integration,residual redundancy,texture location,uncorrelated data,vector quantization
Image texture,Edge detection,Computer science,Image processing,Electronic engineering,Vector quantization,Image restoration,Data compression,Quantization (signal processing),Signal compression
Journal
Volume
Issue
ISSN
43
2
0090-6778
Citations 
PageRank 
References 
6
2.98
9
Authors
4
Name
Order
Citations
PageRank
F. G. B. De Natale1386.93
Giuseppe Desoli238941.91
D. D. Giusto3103.52
G. Vernazza4438.28