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PSNR Estimate for JPEG Compression

机译：JPEG压缩的PSNR估计

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JPEG is wildly used for image compression, which inevitably introduces some distortions, such as blocking artifacts and blurring. Peak Signal to Noise Ratio (PSNR) is the most widely used objective criterion to evaluate image distortion, which is a full reference image quality assessment and requires original image as the reference. However, this requirement cannot always be guaranteed, so that no reference PSNR estimate (NRPE) is required in some applications. NRPE is an ill-pose problem and need some prior knowledge to produce rational results. DCT coefficients are usually assumed with even or Gaussian distributions, and their parameters are estimated by learning or no learning based algorithms in PSNR calculation. These works are unsatisfied for their estimate error is even larger than 3dB for the heavy compressed images. Note that the correlations of image pixels will be destroyed and some artifacts will appear after heavy compression, such as blocking and blurring. In this paper, the relationship of mean squared difference of slope (MSDS), pixel correlation, image variance and the left alternating current (AC) energy is theoretically analyzed, and then PSNR is constructed as the function of MSDS and left AC energy. The left AC energy cannot be exactly measured in decoded image, hence that it is replaced by the index of the last nonzero coefficients for simplicity. Benefit from this arrangement, the proposed algorithm produces more accurate results over the-state-of-art NRPE algorithms.
机译：JPEG被广泛用于图像压缩，这不可避免地会引入一些失真，例如块状伪影和模糊。峰值信噪比（PSNR）是评估图像失真的最广泛使用的客观标准，这是完整的参考图像质量评估，需要原始图像作为参考。但是，不能始终保证这一要求，因此在某些应用中不需要参考PSNR估计（NRPE）。 NRPE是一个不适的问题，需要一些先验知识才能得出合理的结果。通常假定DCT系数具有偶数或高斯分布，并且通过在PSNR计算中基于学习或不学习的算法来估计其参数。这些作品不满意，因为对于重压缩图像，其估计误差甚至大于3dB。请注意，图像像素的相关性将被破坏，并且在重压缩后会出现一些伪像，例如阻塞和模糊。本文从理论上分析了斜率均方差（MSDS），像素相关性，图像方差和左交流电（AC）能量之间的关系，然后将PSNR构造为MSDS和左交流电的函数。剩余的交流能量无法在解码图像中精确测量，因此为简单起见，将其替换为最后一个非零系数的索引。受益于这种安排，与最新的NRPE算法相比，所提出的算法可产生更准确的结果。

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