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New Edge-Directed Interpolation Based-Lifting DWT and MSPIHT Algorithm for Image Compression

机译:基于边缘定向的新插值提升DWT和MSPIHT算法进行图像压缩

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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.
机译:在多媒体应用中生成的图像数据量不断增加。图像压缩在多媒体应用中起着至关重要的作用。图像压缩的最终目的是在不降低图像质量的情况下减少存储空间。只要处理的数据巨大,就可能需要进行压缩,以进行发送或传输以及存储。本文提出了一种新的基于有向插值插值(NEDI)的提升离散小波变换(DWT)方案,该方案具有改进的分层树集划分(MSPIHT)算法。 NEDI算法可提供良好的视觉质量图像,尤其是在边缘处。本文的主要目的是在执行图像压缩时保留边缘,这是一项艰巨的任务。带有提升小波变换的NEDI在低频范围内已达到99.18%的能级,比5/3小波分解高1.07%,比传统小波变换高0.94%。为了实现带有提升DWT的NEDI和MSPIHT算法,该算法可以提供更高的峰值信噪比(PSNR)值和最小均方误差(MSE),从而获得更好的图像质量。实验结果表明,该方法具有较好的PSNR值(速率0.9 bpp,无算术编码时为39.40 dB),最小MSE值为7.4。

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