  Image similarity is a core concept in Image Analysis due to its extensive
application in computer vision, image processing, and pattern recognition. The
objective of our study is to evaluate Quasi-Euclidean metric as an image
similarity measure and analyze how it fares against the existing standard ways
like SSIM and Euclidean metric. In this paper, we analyzed the similarity
between two images from our own novice dataset and assessed its performance
against the Euclidean distance metric and SSIM. We also present experimental
results along with evidence indicating that our proposed implementation when
applied to our novice dataset, furnished different results than standard
metrics in terms of effectiveness and accuracy. In some cases, our methodology
projected remarkable performance and it is also interesting to note that our
implementation proves to be a step ahead in recognizing similarity when
compared to
