International Journal of Emerging Research in Engineering, Science, and Management
Vol. 1, Issue 2, pp. 01-06, Apr-Jun 2022.
https://doi.org/10.58482/ijeresm.v1i2.1

Region Based Non-Uniform Scheme for Effective Retargeting

*Ethireddy Sasikala Reddy

Y Swathi

K Chandini

S Swetha

Ch Swapna

*Assistant Professor, Dept. of ECE, Gokula Krishna College of Engineering, Sullurpeta

UG Scholar, Dept. of ECE, Gokula Krishna College of Engineering, Sullurpeta

Abstract: With the invent of displays of varied features like bendable displays, twistable displays and slant displays the feed of these displays need to be adjusted so that useful objects of the scene are not destroyed. In general, if we want to project a particular scene of specified size after capturing on the display of other dimensions, image resizing is done. In image resizing, the whole image will be converted into dimension of the target display. In the process the whole image, including all the objects may be expanded or squeezed depending on the size of the target display. But, in image retargeting, in addition to changing the size of the image to suit the target display, some additional intelligence will be added there by preserving or retaining useful objects and removing unnecessary background information. In this paper, the global significance map will drive the retargeting which is formed by merging the local significance maps. The local significance maps are derived after forming regions by considering the average intensity levels locally. The simulation results clearly show the superiority of the scheme proposed.

Keywords: — aspect ratio, image resizing, image retargeting, image wrapping, resolution

References:

  1. Amit, D. Geman, and K. Wilder, “Joint induction of shape features and tree classifiers”, Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, November 1997.
  2. Jaya Krishna Sunkara, Uday Kumar Panta, Nagarjuna Pemmasani, Chandra Sekhar Paricherla, Pramadeesa Pattasani, Venkataiah Pattem, “Region Based Active Contour Model for Intensity Non-uniformity Correction for Image Segmentation”, International Journal of Engineering Research and Technology, vol. 6, no. 1, pp. 61-73, 2013.
  3. Sung and T. Poggio, “Example-based learning for view-based face detection,” in IEEE Patt. Anal. Mach. Intell., volume 20, pages 39–51, 1998.
  4. Jaya Krishna Sunkara, E Navaneethasagari, D Pradeep, E Naga Chaithanya, D Pavani, D V Sai Sudheer, “A New Video Compression Method using DCT/DWT and SPIHT based on Accordion Representation”, I.J. Image, Graphics and Signal Processing, pp. 28-34, May 2012.
  5. V. Satyanarayana Tallapragada, B. Bhaskar Reddy, V. Ramamurthy and Jaya Krishna Sunkara, “Effective Compression of Digital Images Using SPIHT Coding with Selective Decomposition Bands”, in First International Conference on Advances in Electrical and Computer Technologies, Springer, Coimbatore, India, pp. 651-655.
  6. Paul Viola and Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” in Conference on Computer Vision and Pattern Recognition 2001.
  7. Itti, L., Koch, C., and Neibur, E. 1999, “A model of saliency based visual attention for rapid scene analysis,” PAMI 20, 11, 1254–1259.
  8. Avidan, S., and Shamir, A. 2007, “Seam carving for content aware image resizing,” ACM Trans. Graph. 26, 3, 10.
  9. Jaya Krishna Sunkara, Purnima Kuruma, Ravi Sankaraiah Y, “Image Compression using Hand Designed and Lifting Based Wavelet Transforms”, International Journal of Electronics Communications and Computer Technology, vol. 2 (4), 2012.
  10. Jaya Krishna Sunkara, E Navaneethasagari, D Pradeep, E Naga Chaithanya, D Pavani, D V Sai Sudheer, “A New Video Compression Method using DCT/DWT and SPIHT based on Accordion Representation”, I.J. Image, Graphics and Signal Processing, pp. 28-34, May 2012.
  11. Gal, R., Sorkine, O., and Cohen-Or, D. 2006, “Feature aware texturing,” in Proceedings of Eurographics Symposium on Rendering, 297–303.
  12. Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee, “Boosting the margin: A new explanation for the effectiveness of voting methods,” in Proceedings of the Fourteenth International Conference on Machine Learning, 1997.
  13. Liu and M. Gleicher, “Video retargeting: automating pan and scan,” in ACM Multimedia, 2006.
  14. Meng, Y. Juan, and S.-F. Chang, “Scene change detection in an MPEG-compressed video sequence,” in Digital Video Compression: Algorithms and Technologies, 1995.
  15. V. Satyanarayana Tallapragada, N. Ananda Rao and Satish Kanapala, “Leaf Disease Detection using Combined Feature of Texture, Colour and Wavelet Transform”, International Journal of Control Theory and Applications, vol. 10, No. 21, 2017, pp. 159-167.