Image Quality Assessment based on Sudoku for Feature-similarity Metric
Gang Liu, Yuan Zhang, Fang Yang & Xuefeng Yang
XiDian University, Xian, Shaanxi, China
ABSTRACT: Although the novel Feature-similarity Metric-based Image quality assessment (FSIM) is simple and has been proved to be better than many traditional methods such as the structure similarity metric (SSIM), the Peak Signal-to-Noise Ratio (PSNR), there are still some difficulties in these distorted pictures with the same type and different levels. We cannot distinguish the differences from the objective values among these images perceived through subjective visual. This paper presents a method based on a new statistics we call it Sudoku for feature-similarity metric (Sudoku-FSIM), ...
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