International Journal For Multidisciplinary Research

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PERFORMANCE BENCHMARKING IN COLOUR MODEL

Author(s) Divyanshi Sharma, Gautam Sharma, Chirag Kumar Jha
Country India
Abstract This study of different colour models in Image processing aims for developing a comprehensive model
that can be used for comparing the vast range of models.
The following comparison will be evaluated on the basis of some critical factors mainly listed as:
accuracy, processing time, usage of space, graphical processing unit(GPU) performance, central
processing unit(CPU) performance and some other relevant metrics. Many research work on colour
models have been done mathematically like a comprehensive study on different available colour models
for suitable computer vision tasks. The core intent of this paper is to analyse the colour models
performance on the basis of the utilisation of a customised dataset. And perform conversion of each
image in all the major colour models, followed by the conversion, we have employed convolutional
neural network (CNN) for object detection tasks, supported by edge detection using morphological
filtering with the operators like dilation and erosion for an enhanced and smoother detection. We have
used YOLO V5 and Mediapipe from google for a lightweight model (key findings) .The significance of
this project helps to identify the effective colour model without having to test out every model for a
required task. Due to this comprehensive comparative table / survey one can choose the model
efficiently for their required colour spaces and object detection task.
Keywords RGB,CMYK,HSV,LAB,YCbCr
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-30
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.37944
Short DOI https://doi.org/g9g74q
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