【阅读笔记】边缘损耗率评价指标《A New Hardware-Efficient Algorithm and Reconfigurable Architecture for Image
论文《A New Hardware-Efficient Algorithm and Reconfigurable Architecture for Image Contrast Enhancement》提到对对比度增强的图像进行客观评价,引用论文《Image Enhancement for Backlight-Scaled TFT-LCD Displays》中的边缘损耗率指标(The edge loss rate)。
原文:Contrast enhancement is not easily measured by quantitative criteria. To judge the preser,vation of image details quantitatively, a measure of the edge loss rates was adopted [13] for the test cases for the seven algorithms.
较低的边缘丢失率值指示对图像细节的更大保留。
1 The edge loss rate
为了对性能进行定量度量,我们设计了两个度量来衡量边缘保存的程度,一个使用Sobel算子,另一个是仅显著差异(just noticeable difference,JND)算法。
前者计算边缘损失率εE,其定义为遗漏边像素数ψEm与原始边缘像素数ψEi之间的比值
adopted for the test cases for the seven algorithms. The edge loss rate εE is defined as the ratio between the number of missed edge pixels ψ Em and the number of original edge pixels ψ Ei .
1.1 sobel算子
该指标计算边缘损失率<script type="math/tex;mode=inline" id="MathJax-Element-5">A</script>A,其定义为丢失像素数<script type="math/tex;mode=inline" id="MathJax-Element-11">E_m</script>E_m与原始边缘像素数<script type="math/tex;mode=inline" id="MathJax-Element-6">E_i</script>E_i之间的比值。边缘信息的提取用到了sobel算子,用横向纵向均可,<script type="math/tex;mode=inline" id="MathJax-Element-10">sobel_x</script>sobel_x和<script type="math/tex;mode=inline" id="MathJax-Element-9">sobel_y</script>sobel_y如下所示:
<script type="math/tex;mode=display" id="MathJax-Element-7">sobel_x= \begin{bmatrix} 1 & 0 & -1\\ 2 & 0 & -2\\ 1 & 0 & -1\\ \end{bmatrix} \\</script>sobel_x= \begin{bmatrix} 1 & 0 & -1\\ 2 & 0 & -2\\ 1 & 0 & -1\\ \end{bmatrix} \\<script type="math/tex;mode=display" id="MathJax-Element-2">sobel_y= \begin{bmatrix} 1 & 2 & 1\\ 0 & 0 & 0\\ -1 & -2 & -1\\ \end{bmatrix} \\</script>sobel_y= \begin{bmatrix} 1 & 2 & 1\\ 0 & 0 & 0\\ -1 & -2 & -1\\ \end{bmatrix} \\<script type="math/tex;mode=display" id="MathJax-Element-8">A=\frac{Em}{Ei} \\</script>A=\frac{Em}{Ei} \\
其中,<script type="math/tex;mode=inline" id="MathJax-Element-1">E_m</script>E_m表示丢失的边缘像素数量,<script type="math/tex;mode=inline" id="MathJax-Element-4">E_i</script>E_i表示原始边缘像素数量。
如果一个像素是原始图像中的边缘像素而不是增强图像中的,则它被定义为丢失边缘像素
1.2 JND算法
第二个度量仅显著差异(just noticeable difference, JND)算法进行边缘像素分类,采用JND边缘像素分类算法来计算边缘丢失率<script type="math/tex;mode=inline" id="MathJax-Element-3">B</script>B
<script type="math/tex;mode=display" id="MathJax-Element-12">B=\frac{Dm}{Di} \\</script>B=\frac{Dm}{Di} \\
其中,<script type="math/tex;mode=inline" id="MathJax-Element-14">D_m</script>D_m表示丢失的边缘像素,<script type="math/tex;mode=inline" id="MathJax-Element-13">D_i</script>D_i表示原始边缘像素数目。
2 后记
看了文章还是不清楚第二个是怎么计算和定义丢失边缘像素,文章直接给出了图像处理前后,边缘像素丢失率的前后对比。
对于文章的指标用途是,文章算法在增强图像对比度的同时对图像细节信息不会降低太多。
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