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An image coding/decoding method based on direct and inverse fuzzy transforms

机译:基于正逆模糊变换的图像编码/解码方法

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With some modifications, we adopt the coding/decoding method of image processing based on the direct and inverse fuzzy transforms defined in previous papers. By normalizing the values of its pixels, any image can be considered as a fuzzy matrix (relation) which is subdivided in submatrices (possibly square) called blocks. Each block is compressed with the formula of the discrete fuzzy transform of a function in two variables and successively it is decompressed via the related inverse fuzzy transform. The decompressed blocks are recomposed for the reconstruction of the image, whose quality is evaluated by calculating the PSNR (Peak Signal to Noise Ratio) with respect to the original image. A comparison with the coding/decoding method of image processing based on the fuzzy relation equations with the Lukasiewicz triangular norm and the DCT method are also presented. By using the same compression rate in the three methods, the results show that the PSNR obtained with the usage of direct and inverse fuzzy transforms is higher than the PSNR determined either with fuzzy relation equations method or in the DCT one and it is close to the PSNR determined in JPEG method for small values of the compression rate.
机译:经过一些修改,我们采用了基于先前论文中定义的直接和逆模糊变换的图像处理编码/解码方法。通过归一化其像素值,可以将任何图像视为模糊矩阵(关系),并将其细分为称为块的子矩阵(可能为正方形)。每个块都使用两个变量的函数离散模糊变换的公式进行压缩,然后通过相关的逆模糊变换对其进行解压缩。对解压缩的块进行重组以重建图像,并通过计算相对于原始图像的PSNR(峰值信噪比)来评估其质量。提出了基于模糊关系方程的Lukasiewicz三角模与DCT方法与图像处理编码/解码方法的比较。在这三种方法中使用相同的压缩率,结果表明,使用直接和逆模糊变换获得的PSNR高于使用模糊关系方程法或DCT所确定的PSNR,并且接近于JPEG方法中针对较小的压缩率值确定的PSNR。

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