Open Access Open Access  Restricted Access Subscription Access

Scaling of Digital Image using Pixel Replication


Affiliations
1 Yadavindra College of Engineering, GKC, Punjabi University, Talwandi Sabo, India
 

Image Interpolation is a basic part to scale the advanced image. The proposed interpolation technique used the back propagation algorithm for picture scaling and zooming. The back propagation algorithm learns from the current pixels and generates the new location of pixels. It calculates the error where error is the difference between actual output and desired output. The pixel that has least error is replicated first. The proposed method is evaluated using image quality metrix (IQM) like Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Signal Noise Ratio (SNR) and Structure Similarity index (SSIM) of scaled image. From experimental result with scaling factor of 1, it has been concluded that the proposed method reduced the complexity 74.96% of an image as compared Gradient Based method reduced complexity of an image is 76.97%. So, proposed method defines better result as compare to Gradient Based method.

Keywords

Nearest Neighbor Interpolation, Bilinear Interpolation, Gradient Based Method, Scaling.
User
Notifications
Font Size

  • Airsang, U. and Ghorpade. 2015. Multistaged Gradient Based Scaling Technique. Proc of the IEEE International Conference on Pervasive Computing.8-10 Jan. Pune: pp. 1-6.
  • Rahim, A. et al. 2015. An Analysis of Interpolation Methods for Supper Resolution Images. Proc. of the IEEE Student Conference on Research and Development. 13-14 Dec. Kuala Lumpur, Malaysia: pp. 72-77.
  • Sabrin, K. and Ali. 2014. An Intelligent Pixel Replication Technique by Binary decomposition for Digital Image Zooming. Proc. of the 26th Image and Vision Computing. 13 May. New Zealand: pp.547-552.
  • Sa, Y. 2014. Improved Bilinear Interpolation Method for Image Fast Processing. Proc. of 7th IEEE International Conference on Intelligent Computation Technology and Automation. 25-26 Oct. Changsha, China: Pp.308 -312.
  • Safinaz. S. 2014. An Efficient Algorithm for Image Scaling with High Boost Filtering. International Journal of Scientific and Research Publication. 4(5): pp. 1-9.
  • Hu, Y. et al. 2012. Image Zooming for Indexed Color Images based on Bilinear Interpolation. International Journal of Multimedia and Ubiquitous Engineering 7(5): pp.353-358.
  • Zhag, X. et al. 2012. Principal Component Analysis-Based Edge-Directed Image Interpolation. Proc. of the IEEE International Conference on Multimedia and Expo. 9-13 July. VIC, Australia: pp.580-585.
  • Zhou, D. Shen, X. and Dong. 2010. Image Zooming using Directional Cubic Convolution Interpolation. IET institution of Engineering and Technology. 6(6): 627-634.
  • Keys, R. 1981. Cubic Convolution Interpolation for Digital Image Processing. Proc. of the IEEE Transactions on Acoustics, Speech, and signal Processing. 29(6): 1153-1160.
  • Mahajan, S. and Harpale. 2015. Adaptive and Non adaptive Image Interpolation Techniques. Proc. of the IEEE International Conference on computing Computation Control and Automation.26-27 Feb. Pune, India: pp.772-775.

Abstract Views: 154

PDF Views: 0




  • Scaling of Digital Image using Pixel Replication

Abstract Views: 154  |  PDF Views: 0

Authors

Sukhveer Kaur
Yadavindra College of Engineering, GKC, Punjabi University, Talwandi Sabo, India
Balkrishan Jindal
Yadavindra College of Engineering, GKC, Punjabi University, Talwandi Sabo, India

Abstract


Image Interpolation is a basic part to scale the advanced image. The proposed interpolation technique used the back propagation algorithm for picture scaling and zooming. The back propagation algorithm learns from the current pixels and generates the new location of pixels. It calculates the error where error is the difference between actual output and desired output. The pixel that has least error is replicated first. The proposed method is evaluated using image quality metrix (IQM) like Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Signal Noise Ratio (SNR) and Structure Similarity index (SSIM) of scaled image. From experimental result with scaling factor of 1, it has been concluded that the proposed method reduced the complexity 74.96% of an image as compared Gradient Based method reduced complexity of an image is 76.97%. So, proposed method defines better result as compare to Gradient Based method.

Keywords


Nearest Neighbor Interpolation, Bilinear Interpolation, Gradient Based Method, Scaling.

References