Evaluating Image Redundancies by Analyzing Pixel Based Similarity Ratios

Abstract

While bifurcating a video at a specific rate and extracting frames from it, there might be an occurrence of repetitive similar frames which can cause redundancy of data. As a result, computation time increases, thus causing problems in determining the most accurate frame for further computation such as content extraction, feature extraction, etc. The intent of this paper is to introduce a solution to detect such lightly identical frames from the input video as well as to detect similar images having equal dimensions from a database. The proposed detection algorithm in this research paper mainly focuses on converting frames to NumPy array objects and then performing array-based numerical operations on them, which also involves comparing a pair of images pixel-by-pixel to determine the similarity ratio between them. It returns a new dataset of frames having minimal duplicate frames, and a compressed version of the original video can be re-constructed using this dataset of frames. The key concept is to eradicate anomalies caused by the redundancy of frames.