A Study on the Effect of Canny Edge Detection on Downscaled Images
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A Study on the Effect of Canny Edge Detection on Downscaled Images Yong Woon Kima,*, Innila Rose Ja,**, and Addapalli V. N. Krishna b,*** a
Centre for Digital Innovation, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bangalore, India b Department of Computer Science and Engineering, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bangalore, India * e-mail: [email protected] ** e-mail: [email protected] *** e-mail: [email protected] Abstract—Nowadays user devices such as phones, tablets etc. allows processing the images with help of highend applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling. Keywords: Canny Edge detection, Gaussian blur, structural similarity index method, image interpolation DOI: 10.1134/S1054661820030116
1. INTRODUCTION The content available on the internet is increasingly becoming image based. From social media sites, e-commerce portals, news portals to scientific journal articles, images form a major part of these contents [1]. In most cases, the images are to be displayed on mobile phones, tablets, laptops and desktops. These display devices have low resolution and therefore the images with high resolution have to be downscaled. If the images doesn’t fit to the aspect ratios of the display devices, the image would be distorted which leads to poor visual interpretation. Additionally, it also takes more time to upload or download original images with high resolution. To make the images suitable for these displays, scaling or resizing is one of the most frequently used image processing operations [2]. The fundamental idea of image scaling is to have a reference image and use this image as the base to construct a new scaled image. The constructed image will be smaller,
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