Lossy compression through segmentation on low depth-of-field images

The popularity of multimedia applications has resulted in development of lossless and lossy compression techniques. This paper presents a novel lossy compression scheme for the low depth-of-field (DOF) images where the quality factor is altered based on whether we are compressing object-of-interest (OOI) or the background. The proposed method involves segmentation of OOI and then the application of lossy scheme. The experimental results shows that the proposed method performs well in compressing the given image (higher compression ratio), at the same time maintaining the acceptable quality (high PSNR).

[1]  Metin Kaya,et al.  An Algorithm for Image Clustering and Compression , 2005 .

[2]  Guillermo Sapiro,et al.  Image filling-in in a decomposition space , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Du-Ming Tsai,et al.  Segmenting focused objects in complex visual images , 1998, Pattern Recognit. Lett..

[4]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Changick Kim,et al.  Extracting focused object from low depth-of-field image sequences , 2006, Electronic Imaging.

[6]  Changick Kim,et al.  Segmenting a low-depth-of-field image using morphological filters and region merging , 2005, IEEE Transactions on Image Processing.

[7]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[8]  James Ze Wang,et al.  Unsupervised Multiresolution Segmentation for Images with Low Depth of Field , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Uwe Martin,et al.  New Perspectives on Image Compression , 1997 .

[10]  Touradj Ebrahimi,et al.  The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..

[11]  Khalid Sayood,et al.  Introduction to Data Compression , 1996 .

[12]  Montse Pardàs,et al.  Hierarchical morphological segmentation for image sequence coding , 1994, IEEE Trans. Image Process..

[13]  G. Delaunay,et al.  Higher order statistics for detection and classification of faulty fanbelts using acoustical analysis , 1997, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics.

[14]  Stephen M. Pizer,et al.  A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema , 1990, IEEE Trans. Pattern Anal. Mach. Intell..