Efficient and secure image authentication with robustness and versatility

Chen, Haixia; Huang, Xinyi; Wu, Wei; Mu, Yi

doi:10.1007/s11432-020-3007-5

Cited by 6 publications

(1 citation statement)

References 40 publications

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“…When an image is converted into an array it has 3 dimensions, height width and depth where depth represents the colour. [11] When a computer retrieves an image, it will first break it down into three separate channels: red , green, and blue. For the development of this algorithm the colour dimension doesn"t help us much, thus images are converted into grayscale images first before converting them into arrays.…”

Section: A Image Processingmentioning

confidence: 99%

Evaluating Image Redundancies by Analyzing Pixel Based Similarity Ratios

Patil¹,

Jagdale²

2022

IJERCSE

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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.

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Section: A Image Processingmentioning

confidence: 99%