基于Haar小波的图像压缩及质量评价
Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image
摘要：
随着技术的不断发展和进入数字时代，我们每次都要处理大量的信息，这往往会带来困难。因此，数字信息必须以高效和有效的方式存储和检索，以便将其投入实际使用。Wavelet提供了一种编码信息的数学方法，其方式是根据细节层次分层。这种分层便于在各个中间阶段进行近似。这些近似值可以使用比原始数据少得多的空间来存储。提出了一种以小波为基函数的低复杂度二维图像压缩方法，并给出了压缩图像质量的度量方法。这里选择和使用的特定小波是最简单的小波形式，即Haar小波。应用二维离散小波变换（DWT），从图像的信息矩阵中估计出细节矩阵。利用小波变换提供的细节矩阵和信息矩阵估计值合成重构图像。通过压缩比、峰值信噪比、平均意见评分、图像质量等级等指标对压缩后的图像质量进行了评价。
英文摘要：
With the increasing growth of technology and the entrance into the digital age, we have to handle a vast amount of information every time which often presents difficulties. So, the digital information must be stored and retrieved in an efficient and effective manner, in order for it to be put to practical use. Wavelets provide a mathematical way of encoding information in such a way that it is layered according to level of detail. This layering facilitates approximations at various intermediate stages. These approximations can be stored using a lot less space than the original data. Here a low complex 2D image compression method using wavelets as the basis functions and the approach to measure the quality of the compressed image are presented. The particular wavelet chosen and used here is the simplest wavelet form namely the Haar Wavelet. The 2D discret wavelet transform (DWT) has been applied and the detail matrices from the information matrix of the image have been estimated. The reconstructed image is synthesized using the estimated detail matrices and information matrix provided by the Wavelet transform. The quality of the compressed images has been evaluated using some factors like Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), Picture Quality Scale (PQS) etc.
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