Lines Matching full:normalized
156 <p>Empirical evidence suggests that the distances in color spaces such as YUV, or YIQ correspond to perceptual color differences more closely than do distances in RGB space. These color spaces may give better results when color reducing an image. Here the algorithm is as described except each pixel is a point in the alternate color space. For convenience, the color components are normalized to the range 0 to a maximum value, <var>Cmax</var>. The color reduction can then proceed as described.</p>
162 <p>To measure the difference between the original and color reduced images (the total color reduction error), ImageMagick sums over all pixels in an image the distance squared in RGB space between each original pixel value and its color reduced value. ImageMagick prints several error measurements including the mean error per pixel, the normalized mean error, and the normalized maximum error.</p>
164 <p>The normalized error measurement can be used to compare images. In general, the closer the mean error is to zero the more the quantized image resembles the source image. Ideally, the error should be perceptually-based, since the human eye is the final judge of quantization quality.</p>
175 <td>normalized mean square error</td>
176 <td>is the normalized mean square quantization error for any single pixel in the image. This distance measure is normalized to a range between 0 and 1. It is independent of the range of red, green, and blue values in the image.</td>
179 <td>normalized maximum square error</td>
180 <td>is the largest normalized square quantization error for any single pixel in the image. This distance measure is normalized to a range between of red, green, and blue values in the image.</td>
