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Comparison of image compression techniques using huffman coding, DWT and fractal algorithm

机译:使用霍夫曼编码,DWT和分形算法的图像压缩技术比较

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Image compression is one of the advantageous techniques in different types of multi-media services. Image Compression technique have been emerged as one of the most important and successful applications in image analysis. In this paper the proposal of image compression using simple coding techniques called Huffman; Discrete Wavelet Transform (DWT) coding and fractal algorithm is done. These techniques are simple in implementation and utilize less memory. Huffman coding technique involves in reducing the redundant data in input images. DWT can be able to improve the quality of compressed image. Fractal algorithm involves encoding process and gives better compression ratio. By using the above algorithms the calculation of Peak signal to noise ratio (PSNR), Mean Square error (MSE) and compression ratio (CR) and Bits per pixel (BPP) of the compressed image by giving 512×512 input images and also the comparison of performance analysis of the parameters with that above algorithms is done. The result clearly explains that Fractal algorithm provides better Compression ratio (CR) and Peak Signal to noise ratio (PSNR).
机译:图像压缩是不同类型的多媒体服务中的有利技术之一。图像压缩技术已被出现为图像分析中最重要和成功的应用之一。在本文中,使用简单的编码技术称为霍夫曼的图像压缩的提议;完成离散小波变换(DWT)编码和分形算法。这些技术在实现中很简单,并利用更少的内存。霍夫曼编码技术涉及减少输入图像中的冗余数据。 DWT可以能够提高压缩图像的质量。分形算法涉及编码过程并提供更好的压缩比。通过使用上述算法计算峰值信号到噪声比(PSNR),均方误差(MSE)和压缩比(CR)和压缩图像每像素(BPP)的比特(BPP)通过给出512×512输入图像,并且完成上述算法的参数的性能分析比较。结果清楚地解释了分形算法提供更好的压缩比(CR)和峰值信号到噪声比(PSNR)。

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