Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation
This work addresses the problem of selecting an optimal color space for color image segmentation, which is incremental as it compares two established methods.
The paper compared the performance of L*A*B* and HSV color spaces for color image segmentation, finding that HSV performed better based on MSE and PSNR metrics.
Color image segmentation is a very emerging topic for image processing research. Since it has the ability to present the result in a way that is much more close to the human yes perceive, so todays more research is going on this area. Choosing a proper color space is a very important issue for color image segmentation process. Generally LAB and HSV are the two frequently chosen color spaces. In this paper a comparative analysis is performed between these two color spaces with respect to color image segmentation. For measuring their performance, we consider the parameters: mse and psnr . It is found that HSV color space is performing better than LAB.