We undoubtedly have a chunk of images on our computer. The problem with having a lot of pictures is that you tend to accumulate duplicates along the way. It would be prudent to manage space efficiently. Detecting duplicate images from a set of images is a timeconsuming task that can be automated, and duplicate data can be removed to save space. As we use our phones more, the number of unwanted duplicate photo and picture files grows in the device at random, ideally in every folder. Duplicate photos/pictures consume a lot of phone memory and slow down the phone's performance. Finding and removing them manually is difficult. Since human visual ability is not well developed enough to extract structure similarity from the naked eye, we propose a novel approach based on structural information degradation. As a practical solution to this problem, we create a structural similarity index and demonstrate it with a set of images from our database. Finding similar and duplicate photos from these samples can be a time-consuming task. Duplicate photo finders come in handy in this situation. Finally, we will compare the computation time and power required by processing on multiple cores vs. single core threads, as well as provide benchmarks and graphical representations for each.

Keywords : Single core ; Multithreading ; Multiprocessing; RGB ; Luminance ; Contrast ; Structure ; Similarity Index