Lines Matching refs:pixel
61 <p>For purposes of color allocation, an image is a set of n pixels, where each pixel is a point in RGB space. RGB space is a 3-dimensional vector space, and each pixel, Pi, is defined by an ordered triple of red, green, and blue coordinates, (Ri, Gi, Bi).</p>
69 <p>The basic algorithm operates in three phases: Classification, Reduction, and Assignment. Classification builds a color description tree for the image. Reduction collapses the tree until the number it represents, at most, the number of colors desired in the output image. Assignment defines the output image's color map and sets each pixel's color by restorage_class in the reduced tree. Our goal is to minimize the numerical discrepancies between the original colors and quantized colors (quantization error).</p>
77 <p>For each pixel in the input image, storage_class scans downward from the root of the color description tree. At each level of the tree it identifies the single node which represents a cube in RGB space containing the pixel's color. It updates the following data for each such node:</p>
88 <p>E: the distance squared in RGB space between each pixel contained within a node and the nodes' center. This represents the quantization error for a node.</p>
101 <dd> For each node, n2 pixels exist for which that node represents the smallest volume in RGB space containing those pixel's colors. When n2 > 0 the node will uniquely define a color in the output image. At the beginning of reduction, n2 = 0 for all nodes except a the leaves of the tree which represent colors present in the input image. </dd>
103 <dd> The other pixel count, n1, indicates the total number of colors within the cubic volume which the node represents. This includes n1 - n2 pixels whose colors should be defined by nodes at a lower level in the tree. </dd>
108 <dd>(1) A color map, which is an array of color descriptions (RGB triples) for each color present in the output image; (2) A pixel array, which represents each pixel as an index into the color map array. </dd>
112 <dd> Finally, the assignment phase reclassifies each pixel in the pruned tree to identify the deepest node containing the pixel's color. The pixel's value in the pixel array becomes the index of this node's mean color in the color map. </dd>
217 <p>GetImageQuantizeError() measures the difference between the original and quantized images. This difference is the total quantization error. The error is computed by summing over all pixels in an image the distance squared in RGB space between each reference pixel value and its quantized value. These values are computed:</p>
221 pixel in the image.
225 <p>This value is the normalized mean 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.</p>
228 <p>Thsi value is the normalized maximum 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 your image.</p>
