Abstract
The amount of image data generated in multimedia applications is ever increasing. The image compression plays vital role in multimedia applications. The ultimate aim of image compression is to reduce storage space without degrading image quality. Compression is required whenever the data handled is huge they may be required to sent or transmitted and also stored. The New Edge Directed Interpolation (NEDI)-based lifting Discrete Wavelet Transfrom (DWT) scheme with modified Set Partitioning In Hierarchical Trees (MSPIHT) algorithm is proposed in this paper. The NEDI algorithm gives good visual quality image particularly at edges. The main objective of this paper is to be preserving the edges while performing image compression which is a challenging task. The NEDI with lifting DWT has achieved 99.18% energy level in the low frequency ranges which has 1.07% higher than 5/3 Wavelet decomposition and 0.94% higher than traditional DWT. To implement this NEDI with Lifting DWT along with MSPIHT algorithm which gives higher Peak Signal to Noise Ratio (PSNR) value and minimum Mean Square Error (MSE) and hence better image quality. The experimental results proved that the proposed method gives better PSNR value (39.40 dB for rate 0.9 bpp without arithmetic coding) and minimum MSE value is 7.4.
Highlights
Today multimedia applications heavily rely on image data’s in which needs efficient image compression techniqueHow to cite this paper: Varathaguru, M. and Sabeenian, R.S. (2016) New Edge-Directed Interpolation Based-Lifting Discrete Wavelet Transform (DWT) and modified Set Partitioning In Hierarchical Trees (MSPIHT) Algorithm for Image Compression
new edge directed interpolation (NEDI) algorithm is based on geometric duality between Low resolutions (LR) and High Resolutions (HR) image
1) The first work of this paper is based on new edge directed interpolation method with lifting DWT scheme
Summary
Today multimedia applications heavily rely on image data’s in which needs efficient image compression technique. The conventional method of 2D-DWT is applied to sheared blocks these provide vanishing moments along the edges [13] This problem can be efficiently handled another technique called directionlets with simple filter design. Because the above said two methods are handling images with small blocks which was produced again the blocking artifacts in the reconstruction of an image To overcome these limitations by using bandelets technique proposed in [11] [12], the directional correlation in the detailed wavelet coefficients removed by using conventional DWT after bandeletization procedure is followed. The conventional DWT has been applied before doing bandeletization because post processing technique still based on wavelet transform Another important technique called lifting scheme is well suited to overcome the above said problems.
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