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  2. Structural similarity index measure - Wikipedia

    en.wikipedia.org/wiki/Structural_similarity...

    In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation.

  3. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others.

  4. Czenakowski distance - Wikipedia

    en.wikipedia.org/wiki/Czenakowski_distance

    The Czenakowski distance (sometimes shortened as CZD) is a per-pixel quality metric that estimates quality or similarity by measuring differences between pixels.Because it compares vectors with strictly non-negative elements, it is often used to compare colored images, as color values cannot be negative.

  5. Jaccard index - Wikipedia

    en.wikipedia.org/wiki/Jaccard_index

    In this scenario, the similarity between the two baskets as measured by the Jaccard index would be 1/3, but the similarity becomes 0.998 using the SMC. In other contexts, where 0 and 1 carry equivalent information (symmetry), the SMC is a better measure of similarity.

  6. Visual comparison - Wikipedia

    en.wikipedia.org/wiki/Visual_comparison

    This technique is good for finding edits in graphical images, or for comparing an image with a compressed version to spot artefacts. [ 3 ] Visual comparison with a standard chart or reference is often used as a means of measuring complex phenomena such as the weather , sea states or the roughness of a river. [ 4 ]

  7. Sum of absolute differences - Wikipedia

    en.wikipedia.org/wiki/Sum_of_absolute_differences

    These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition, the generation of disparity maps for stereo images, and motion estimation for video ...

  8. Content-based image retrieval - Wikipedia

    en.wikipedia.org/wiki/Content-based_image_retrieval

    The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies ...

  9. Dice-Sørensen coefficient - Wikipedia

    en.wikipedia.org/wiki/Dice-Sørensen_coefficient

    Other variations include the "similarity coefficient" or "index", such as Dice similarity coefficient (DSC). Common alternate spellings for Sørensen are Sorenson , Soerenson and Sörenson , and all three can also be seen with the –sen ending (the Danish letter ø is phonetically equivalent to the German/Swedish ö, which can be written as oe ...