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Applied two Stages Minimize-Matrix-Size Algorithm with DCT on DWT for Image Compression

机译:在DWT上应用带DCT的两阶段最小化矩阵尺寸算法进行图像压缩

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As the use of digital imaging is on the rise, compression of acquired digital image data is becoming more and more important to cope with the storage requirements. One of the challenges of compression is to compress images with high efficiently while preserving critical data from getting permanently lost in reconstructed images. In this research we introduce a proposed algorithm for image compression, based on the Minimize-Matrix-Size Algorithm for coding and Limited Sequential Search-Algorithm (LSS-Algorithm) for decoding. The proposed algorithm starts by using single stage Discrete Wavelet Transform, to decompose an image into four subbands; low frequency and high frequencies. Each "n × n" of the low-frequency subband (LL) are transformed by using two dimensional DCT, store all DC coefficients in different matrix called DC-Matrix, and the remain AC coefficients are stored in different matrix called AC-Matrix, then applying Minimize-Matrix-size algorithm for the AC-Matrix, to convert each group of AC coefficients into single floating point value. The DC-Matrix transformed again by DWT, and apply Minimize-Matrix-Size algorithm on it. LSS-Algorithm which is represents decoding; DC-Matrix and AC-Matrix, this algorithm is used to estimate original values by using iterative method, which is depends on the probability of the data of the AC-Matrix and DC-Matrix. Our compression algorithm proved good compression ratio, and compared with JPEG and JPEG2000 depending on the PSNR and HVS.
机译:随着数字成像的使用不断增加,为了满足存储需求,对获取的数字图像数据进行压缩变得越来越重要。压缩的挑战之一是高效压缩图像,同时保留关键数据以免在重建图像中永久丢失。在这项研究中,我们介绍一种基于最小化矩阵大小编码算法和有限顺序搜索算法(LSS-Algorithm)进行解码的图像压缩算法。该算法从单级离散小波变换开始,将图像分解为四个子带。低频和高频。通过使用二维DCT对低频子带(LL)的每个“ n×n”进行变换,将所有DC系数存储在称为DC-Matrix的不同矩阵中,其余的AC系数存储在称为AC-Matrix的不同矩阵中,然后将最小矩阵大小算法应用于AC矩阵,以将每组AC系数转换为单个浮点值。 DWT再次转换了DC-Matrix,并对其应用了Minimize-Matrix-Size算法。 LSS算法代表解码; DC-Matrix和AC-Matrix,此算法用于通过迭代方法估计原始值,这取决于AC-Matrix和DC-Matrix数据的概率。我们的压缩算法证明了良好的压缩率,并且根据PSNR和HVS与JPEG和JPEG2000相比。

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