Comparative Study and Analysis of DWT-SPIHT with DWT-EZW Method for Image Compression

Dikendra K. Verma, Garima Singh, Saurabh Pargaien, Purushottam Das, Sashank Chaube, Upendra Bhatt
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Abstract

The use of digital photographs has increased along with the development of digital technologies. Due to the vast amounts of information it contains, digital photographs need a lot of storage space, as well as bigger transmission bandwidths and longer transmission times. Therefore, on compressing the images all the redundant bits of information present in the image under test are removed while keeping only the essential information needed to reconstruct the image later on. In this study, DWT-SPIHT technique is introduced, which may be used to compress and reconstruct images at various degrees of wavelet decomposition across wavelet families that were initially a subdivision of the MATLAB wavelet family. Simulations have been conducted on Cameraman Image during this work of different resolution at different levels of decomposition and for different types of thresholding techniques to prove that this algorithm works well and provide us with the good reconstruction quality of the image. The simulation results demonstrate that, when compared to the DWT-EZW algorithm, the proposed DWT-SPIHT algorithm performs significantly better in terms of evaluation parameters like peak signal to noise ratio (PSNR), mean square error (MSE), and visual perception at higher compression ratios (CR) and low bit per pixel values (BPP).
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DWT-SPIHT与DWT-EZW图像压缩方法的比较研究与分析
随着数码技术的发展,数码照片的使用也越来越多。由于数码照片所包含的信息量巨大,因此需要很大的存储空间,需要更大的传输带宽和更长的传输时间。因此,在压缩图像时,被测试图像中存在的所有冗余信息位都被删除,而只保留稍后重建图像所需的基本信息。在本研究中,引入了DWT-SPIHT技术,该技术可用于跨小波族进行不同程度的小波分解压缩和重建图像,这些小波族最初是MATLAB小波族的细分。在此过程中对Cameraman图像进行了不同分辨率、不同分解层次和不同阈值化技术的仿真,证明了该算法的有效性,并为我们提供了良好的图像重建质量。仿真结果表明,与DWT-EZW算法相比,所提出的DWT-SPIHT算法在高压缩比(CR)和低像素比特值(BPP)下,在峰值信噪比(PSNR)、均方误差(MSE)和视觉感知等评价参数方面表现明显更好。
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