Background: Image processing strategy is an important part
of image processing to visualise the performance and outcome of the goal. Image
processing is a discipline in which the process's input and output are both
images. It is a process that entails elementary operations such as noise
reduction, contrast enhancement, and image sharpening. Image analysis is a
process that takes images as inputs but produces attributes extracted from
those images as outputs (e.g., edges, contours, and the identity of individual
objects).
Aims: This paper aims to analyse the algorithms of image
processing in the cloud platform. Several algorithms are commonly used in image
processing and computing techniques. Correlations for the observation matrix
were observed to marginalise the images, and results were transmitted to the
cloud platform.
Methodology: Here, a selection of state-of-art is applied to
test image processing execution and timing factor using different strategies
and platforms. Among them, the dataset structure and performance of the system
can choose a verification algorithm to achieve the final operation. Based on
the structure of a real-time image processing system based on SOPC technology
is built, and the corresponding functional receiving unit is designed for
real-time image storage, editing, viewing, and analysis. Datasets were
collected online from the free domain of kaggle.com. Images belong to the
traffic light of 250 out of 2056 files. 120 images were selected randomly to
process after pre-processing of the images.
Results: Studies have shown that the image processing system
based on cloud computing has increased the speed of image data processing by
12.7%. Compared with another platform, especially in the case of segmentation
and enhancement of the image. This analysis has advantages in image compression
and image restoration on a cloud platform. Qualitative and quantitative
performances in the cloud platform of the algorithm are compared, and the
results of the three indicators show that the platform has better performance
than others. The results show that the cloud platform requires less computational
time in comparison with others after loading the image file into the system.
Conclusion: Different image processing parameters like
noise, smoothing, the timing of enhancement and segmentation have a greater
effect on the compression effect of the image, including correlational value
within the dataset of the image. The larger the correlation, the less
compressed the image data is, the faster the image compression rate, and the
lower the image's peak entry-to-noise ratio.
Author(s) Details
Faizur Rashid
Department of Computer Science and Engineering, SCOS, JSPM University,
Pune, India.
Gavendra Singh
Department of Computer Science and Engineering, SDGI Global University,
U.P., India.
Jemal Abate
Department of Computer Science, University of Illinois, Brazil.
Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/5885
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